Updated on 2025/11/25

写真a

 
MASUYAMA Naoki
 
Organization
Graduate School of Informatics Department of Core Informatics Associate Professor
School of Engineering Department of Information Science
Title
Associate Professor
Affiliation
Institute of Informatics
Homepage
https://masuyama-lab.github.io
Affiliation campus
Nakamozu Campus

Position

  • Graduate School of Informatics Department of Core Informatics 

    Associate Professor  2022.10 - Now

  • Graduate School of Informatics Department of Core Informatics 

    Assistant Professor  2022.04 - 2022.09

  • School of Engineering Department of Information Science 

    Associate Professor  2022.10 - Now

  • School of Engineering Department of Information Science 

    Assistant Professor  2022.04 - 2022.09

Degree

  • Ph.D. (Computer Science) （ Others ） (   University of Malaya (Malaysia) )

  • 修士 （ Tokyo Metropolitan University ）

Research Areas

  • Informatics / Intelligent informatics  / Artificial Intelligence

  • Informatics / Intelligent informatics

Research Interests

  • Continual Learning

  • Data Mining

  • Clustering

Research subject summary

  • トポロジカルクラスタリング手法による進化型多目的最適化手法の探索能力の改善

  • 継続的学習が可能な人工知能に関する研究

Research Career

  • トポロジカルクラスタリング手法による進化型多目的最適化手法の探索能力の改善

    多目的最適化、トポロジカルクラスタリング  Individual

    2019.04 - Now 

  • 継続的学習が可能な人工知能に関する研究

    Individual

    2017.10 - Now 

Professional Memberships

  • 進化計算学会

    2020.10 - Now   Domestic

  • Japan Society for Fuzzy Theory and Intelligent Informatics

    2018.09 - Now   Domestic

  • 米国電気電子学会（IEEE）

    2012.04 - Now   Overseas

Committee Memberships (off-campus)

  • 日本知能情報ファジィ学会関西支部   会計担当  

    2023.04 - 2025.03 

  • 継続学習と知能の創発研究会   会計幹事  

    2021.04 - Now 

  • 日本知能情報ファジィ学会事業委員会   会計担当  

    2019.04 - 2025.03 

  • 日本知能情報ファジィ学会   事業委員  

    2019.04 - Now 

Awards

  • Emerging Research Leader Award

    Naoki Masuyama

    2024.11   Japan Society for Fuzzy Theory and Intelligent Informatics  

  • Emerging Research Leader Award

    Naoki Masuyama

    2024.11  

  • 2024年度大阪公立大学若手研究者奨励賞（基礎科学部門）

    増山直輝

    2024.08   大阪公立大学  

  • Competition 3rd Place

    Y. Azuma, T. Kinoshita, N. Masuyama, Y. Nojima

    2024.06   IEEE WCCI (CEC) 2024 Competition on Multi-Objective Black-Box Optimization Benchmarks in Human-Powered Aircraft Design   Competition on Multi-Objective Black-Box Optimization Benchmarks in Human-Powered Aircraft Design

  • Best Paper Award

    T. Konishi, N. Masuyama, and Y. Nojima

    2023.08   20th World Congress of the International Fuzzy Systems Association Award Committee   Effects of complexity enhancements on the search performance of multiobjective fuzzy genetics-based machine learning

  • The Springer Best Paper Award - First Prize

    2019.09   International Conference on Evolutionary Multi-Criterion Optimization  

▼display all

Job Career (off-campus)

  • Osaka Metropolitan University   Department of Core Informatics, Graduate School of Informatics

    2022.10 - Now

  • Osaka Metropolitan University   Department of Core Informatics, Graduate School of Informatics

    2022.04 - 2022.09

  • Osaka Prefecture University   Graduate School of Engineering

    2017.10 - 2022.03

Education

  • University of Malaya   Faculty of Computer Science and Information Technology   Doctor's Course   Graduated/Completed

    2013.03 - 2016.04

  • Tokyo Metropolitan University   The first semester of doctoral program   Graduated/Completed

    - 2012.03

Papers

  • 継続学習と知能の創発研究会発足にあたって

    戸田 雄一郎, 増山 直輝

    知能と情報   37 ( 2 )   27 - 27   2025.05（ ISSN:13477986 ） （ eISSN:18817203 ）

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    International / domestic magazine：Domestic journal  

    DOI： 10.3156/jsoft.37.2_27

    CiNii Research

  • Topological Mapping and Continual Learning

    KUBOTA Naoyuki, OBO Takenori, TODA Yuichiro, MASUYAMA Naoki

    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics   37 ( 2 )   28 - 36   2025.05（ ISSN:13477986 ） （ eISSN:18817203 ）

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    International / domestic magazine：Domestic journal  

    DOI： 10.3156/jsoft.37.2_28

    CiNii Research

  • Multicollinearity-Based Attribute Selection in Multiobjective Fuzzy Genetics-Based Machine Learning for Fish Habitat Assessment Reviewed

    TAKASAKI Shion, FUKUDA Shinji, MASUYAMA Naoki, NOJIMA Yusuke

    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics   37 ( 1 )   524 - 529   2025.02（ ISSN:13477986 ） （ eISSN:18817203 ）

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    Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

    <p>Anthropogenic impacts such as urbanization and water pollution pose significant threats to freshwater ecosystems, leading to habitat degradation and biodiversity loss. Understanding species-habitat relationships is crucial for the conservation and restoration of these ecosystems. In this study, we applied Multiobjective Fuzzy Genetics-based Machine Learning (MoFGBML) to develop interpretable species distribution models for assessing the habitat suitability of five freshwater fish species. Since the number of attributes in the training data can significantly affects both the interpretability and classification accuracy of the fuzzy models, we implemented a multicollinearity-based attribute selection process using the variance inflation factor (VIF) and correlation matrix to identify and remove redundant attributes of the data. Our results demonstrate that the proposed attribute selection clearly reduces the model complexity at the small risk of the model accuracy. This provides a more transparent understanding of habitat suitability for target fish species.</p>

    DOI： 10.3156/jsoft.37.1_524

    CiNii Research

  • A study on fuzzy classifier design considering interpretability and fairness by a quality diversity algorithm

    KONISHI Takeru, MASUYAMA Naoki, NOJIMA Yusuke

    Proceedings of the Annual Conference of JSAI   JSAI2025 ( 0 )   4I1GS1102 - 4I1GS1102   2025（ eISSN:27587347 ）

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    Publishing type：Research paper (conference, symposium, etc.)   International / domestic magazine：Domestic journal  

    <p>There is a growing interest in various aspects of artificial intelligence, beyond accuracy, to address the ethical and social risks. Among them, transparency and fairness are important, and considering fairness in highly transparent models has attracted much attention. Especially, considering fairness in intrinsically interpretable models is expected to be used in cases where transparency and fairness are required as it enables us to consider fairness together with an understanding of the internal mechanism of the model. Fuzzy classifiers are representative intrinsically interpretable models that can make decisions taking into account real-world uncertainties. In this study, we study the fuzzy classifier design by MAP-Elites, a representative quality diversity algorithm, considering interpretability and fairness. We show that by using MAP-Elites, which can improve the performance together with the diversity of selected features, it is possible to obtain a highly accurate set of fuzzy classifiers with high diversity in terms of interpretability and fairness. Furthermore, we discuss the relationship between accuracy, interpretability, and fairness using the feature-performance map.</p>

    DOI： 10.11517/pjsai.jsai2025.0_4i1gs1102

    CiNii Research

  • A decomposition-based constrained multi-objective evolutionary algorithm with adaptive weight adjustment Reviewed

    Y. Azuma, N. Masuyama, Y. Nojima

    Proc. of the 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems   1 - 5   2024.11

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    Publishing type：Research paper (international conference proceedings)  

  • Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory Reviewed

    Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

    IEEE Access   12   139692 - 139710   2024.09（ eISSN:2169-3536 ）

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    Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/access.2024.3467114

  • Clustering-based automatic codeword length determination in self-supervised learning Reviewed

    T. Takebayashi, N. Masuyama, Y. Nojima

    Proc. of the 2024 International Conference on Machine Learning and Cybernetics   1 - 6   2024.09

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    Authorship：Corresponding author   Publishing type：Research paper (international conference proceedings)  

  • Fast Multi-scale Batch-Learning Growing Neural Gas

    Takenori Obo, Naoyuki Kubota, Yuichiro Toda, Naoki Masuyama

    Topics in Intelligent Engineering and Informatics   13 - 33   2024.08（ ISSN:2193-9411 ） （ ISBN:9783031582561, 9783031582578 ） （ eISSN:2193-942X ）

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    Publishing type：Part of collection (book)  

    DOI： 10.1007/978-3-031-58257-8_2

  • Adaptive Resonance Theory-Based Global Topological Map Building for an Autonomous Mobile Robot Reviewed

    Yuichiro Toda, Naoki Masuyama

    IEEE Access   12   111371 - 111385   2024.08（ eISSN:2169-3536 ）

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/access.2024.3442304

  • Fairness-aware Classifier Design via Multi-objective Fuzzy Genetics-based Machine Learning Reviewed

    Takeru Konishi, Naoki Masuyama, Jorge Casillas, Yusuke Nojima

    2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)   1 - 8   2024.06（ ISSN:10987584 ） （ ISBN:9798350319545 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/fuzz-ieee60900.2024.10611911

  • Verification of the Effectiveness of Using an Archive Population on Two-Stage Fuzzy Genetics-Based Machine Learning

    KONISHI Takeru, MASUYAMA Naoki, NOJIMA Yusuke

    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics   36 ( 1 )   565 - 570   2024.02（ ISSN:13477986 ） （ eISSN:18817203 ）

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    Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

    <p>Multi-objective fuzzy genetics-based machine learning can efficiently obtain a set of fuzzy classifiers considering the maximization of classification performance and the minimization of the model complexity by using an evolutionary multi-objective optimization method. However, multi-objective fuzzy genetics-based machine learning has a strong bias towards minimizing complexity in the optimization process, making it difficult to generate classifiers with high classification performance. In our previous study, two-stage fuzzy genetics-based machine learning has been proposed to mitigate this bias: first, an accuracy-oriented single-objective optimization is performed, and then a multi-objective optimization is performed to maximize the classification performance and minimize the complexity. The use of an archive population has also been proposed to obtain a better set of classifiers in two-stage fuzzy genetics-based machine learning. However, the effects of the use of an archive population on a set of classifiers obtained by two-stage fuzzy genetics-based machine learning have not been fully investigated. In this paper, we investigate the effects through computational experiments on a wide variety of real-world datasets.</p>

    DOI： 10.3156/jsoft.36.1_565

    CiNii Research

  • Improvement of a Classifier Using Adaptive Resonance Theory-Based Clustering for Multi-Label Mixed Data

    NISHIKAWA Tsuyoshi, MASUYAMA Naoki, NOJIMA Yusuke

    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics   36 ( 1 )   543 - 549   2024.02（ ISSN:13477986 ） （ eISSN:18817203 ）

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    Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

    <p>Various multi-label classifiers have been proposed for multi-label classification problems. Our previous study has proposed an adaptive resonance theory (ART)-based clustering method using correntropy-induced metric (CIM) as a similarity measure, called CIM-based ART for Multi-Label Mixed Data (CA-MLMD). CA-MLMD adaptively and continually generates nodes corresponding to input data, and the generated nodes are used as a classifier. Moreover, CA-MLMD learns new data and label information continually and handles mixed datasets that contain both numerical and categorical attributes. However, CA-MLMD is highly affected by local data points around the node in learning categorical attributes, which may deteriorate classification performance. This study proposes CA-MLMD-weight (CA-MLMD-w), which uses weights defined by categorical attributes of each node and reduces effects of local data points by considering categorical attributes of the entire data. Numerical experiments on real-world datasets show the effectiveness of the proposed method.</p>

    DOI： 10.3156/jsoft.36.1_543

    CiNii Research

  • Integrating white and black box techniques for interpretable machine learning Reviewed

    E. M. Vernon, N. Masuyama, and Y. Nojima

    Proc. of the 9th International Congress on Information and Communication Technology   2024.02

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

  • A Study on Threshold Optimization Methods in Fuzzy Classifiers with a Threshold-based Reject Option

    Shimahara Hajime, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   40 ( 0 )   572 - 577   2024

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    <p>In cases where misclassification can have serious consequences, such as healthcare and finance, classification systems must ensure high interpretability and reliability. Fuzzy classifiers can provide lin-(breakpoint)guistic explanations for their classification results, thus offering high interpretability. To improve their reliability, we have proposed fuzzy classifiers with a threshold-based reject option, which rejects classifica-(breakpoint)tions with low confidence. A traditional method optimizes the thresholds using constrained single-objective optimization. However, there is a possibility of getting trapped in local optima. In this study, we formu-(breakpoint)late the threshold optimization as a bi-objective optimization problem to minimize misclassification and rejection rates. We apply an evolutionary multi-objective optimization algorithm to this problem in order to find the optimal thresholds to improve the reliability and classification performance of fuzzy classifiers.</p>

    DOI： 10.14864/fss.40.0_572

    CiNii Research

  • A Comparison of Data Selection Methods for Replay Buffer in Continual Learning

    Fujii Ryosuke, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   40 ( 0 )   324 - 327   2024

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    <p>Continual learning aims to learn a sequence of tasks without forgetting the previously learned tasks. One way to achieve continual learning is by applying a replay buffer that stores previously learned samples to existing learning frameworks. Among these methods, Lifelong Unsupervised Mixup (LUMP) interpolates the current task samples and samples from the replay buffer to prevent forgetting. LUMP randomly selects data for the replay buffer. This random selection is likely to cause a biased data dis-(breakpoint)tribution in the buffer, which has a negative effect on learning performance. In this paper, we propose a clustering-based data selection method for the replay buffer. Additionally, we compare several clustering methods because the effect on learning performance is presumed to depend on clustering methods.</p>

    DOI： 10.14864/fss.40.0_324

    CiNii Research

  • Effects of Attribute Selection in Multiobjective Fuzzy Genetics-based Machine Learning on Fish Habitat Assessment

    Takasaki Shion, Fukuda Shinji, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   40 ( 0 )   754 - 759   2024

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    <p>Urbanization, field development, and water pollution have deteriorated aquatic environments. Therefore, the conservation and restoration of ecosystems are urgent issues in preserving biodiversity. To address these issues, it is important to model the environmental preferences of aquatic organisms and evaluate their habitats. Multiobjective Fuzzy Genetics-based Machine Learning (MoFGBML) can generate interpretable models composed of fuzzy if-then rules to assess the environmental preferences of aquatic organisms. The number of attributes in training data significantly affects the interpretability and classification accuracy of fuzzy models. In this paper, we evaluate the effects of attribute selection on the model performance of MoFGBML and the resulting fuzzy if-then rules that explain environmental preferences of a species.</p>

    DOI： 10.14864/fss.40.0_754

    CiNii Research

  • A Study on Fuzzy Classifier Design using MAP-Elites

    Konishi Takeru, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   40 ( 0 )   566 - 571   2024

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    <p>In pattern classification problems, it is becoming increasingly important to have a highly transparent classifier that enables us to understand the process and basis for classification. A fuzzy classifier has high transparency and can make decisions considering the uncertainties of the real world. Evolutionary computation has been actively used in fuzzy classifier design under the name of evolutionary fuzzy systems. MAP-Elites, an algorithm inspired by evolutionary computation, can search for optimal solutions while maintaining diversity in a predefined feature space. In this paper, we study fuzzy classifier design using MAP-Elites, which searches for accurate classifiers in the feature space based on the transparency-related complexity measures. We try to obtain a set of fuzzy classifiers with high accuracy and high diversity of transparency and further investigate the relationship between accuracy and transparency.</p>

    DOI： 10.14864/fss.40.0_566

    CiNii Research

  • Importance of Temporal Information in Fish Habitat Assessment Using Multiobjective Fuzzy Genetics-Based Machine Learning.

    Yusuke Nojima, Shion Takasaki, Shinji Fukuda, Naoki Masuyama

    iFUZZY   1 - 6   2024（ ISBN:9798350352788 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/iFUZZY63051.2024.10662874

    Other URL： https://dblp.uni-trier.de/db/conf/ifuzzy/ifuzzy2024.html#NojimaTFM24

  • Hierarchical Fuzzy Classifier Design Using a Reject Option.

    Rowan Fuerst, Naoki Masuyama, Yusuke Nojima

    FUZZ   1 - 7   2024（ ISSN:10987584 ） （ ISBN:9798350319545 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/FUZZ-IEEE60900.2024.10612164

    Other URL： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2024.html#FuerstMN24

  • CNN-LSTM for Heartbeat Sound Classification

    Aji N.B.

    International Journal on Informatics Visualization   8 ( 2 )   735 - 741   2024

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  • A Growing Hierarchical Clustering Algorithm via Parameter-free Adaptive Resonance Theory. Reviewed

    Kazuki Tashiro, Naoki Masuyama, Yusuke Nojima

    IJCNN   1 - 6   2024（ ISBN:9798350359312 ）

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    Authorship：Corresponding author   Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/IJCNN60899.2024.10650253

    Other URL： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2024.html#TashiroMN24

  • A Federated Data-driven Multiobjective Evolutionary Algorithm via Continual Learnable Clustering Reviewed

    Takato Kinoshita, Naoki Masuyama, Yusuke Nojima

    2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings   1 - 6   2024（ ISBN:9798350308365 ）

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    Publishing type：Research paper (international conference proceedings)  

    As solution evaluation costs increase, data-driven multiobjective optimization is becoming more critical. Using multiple computers in parallel helps manage computation times. Federated learning offers a privacy-friendly, cost-effective way to handle distributed data. Integration of these two approaches is known as federated data-driven multiobjective optimization. However, to the best of our knowledge, a few studies tackle this emerging topic. This paper introduces multiobjective evolutionary algorithms (MOEAs) to federated clustering via adaptive resonance theory-based clustering (FCAC) and proposes FCAC-MOEA as a solver system of federated data-driven optimization problems. The computational experiments showed that the proposed method achieves both high search efficiency and privacy preservation on various multiobjective optimization problems.

    DOI： 10.1109/CEC60901.2024.10611787

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2024.html#KinoshitaMN24

  • Integrating White and Black Box Techniques for Interpretable Machine Learning

    Vernon E.M.

    Lecture Notes in Networks and Systems   1014 LNNS   639 - 649   2024（ ISSN:23673370 ） （ ISBN:9789819735617 ）

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  • Enhancing Machine Learning Interpretability Through a Graphical User Interface

    Tabouret A.K.

    2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024   2024（ ISBN:9798350373332 ）

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  • Constrained Multi-Modal Multi-Objective Evolutionary Algorithm with Problem Transformation into Two-Objective Subproblems

    Tokusaka T.

    2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024   2024（ ISBN:9798350373332 ）

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  • A Decomposition-based Constrained Multi-objective Evolutionary Algorithm with Adaptive Weight Adjustment

    Azuma Y.

    2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024   2024（ ISBN:9798350373332 ）

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  • Riesz s-energy indicators for diversity assessment of multiobjective evolutionary algorithms Reviewed

    T. Kinoshita, N. Masuyama, and Y. Nojima

    Proc. of the 24th International Symposium on Advanced Intelligent Systems   2023.12

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

  • Effects of parent selection schemes on the search performance of multi-modal multi-objective evolutionary algorithm with problem transformation into two-objective subproblems Reviewed

    Y. Nojima, T. Tokusaka, and N. Masuyama

    Proc. of the 24th International Symposium on Advanced Intelligent Systems   2023.12

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

  • Class-wise classifier design capable of continual learning using adaptive resonance theory-based topological clustering Reviewed International coauthorship

    N. Masuyama, Y. Nojima, F. Dawood, and Z. Liu

    Applied Sciences   13 ( 21 )   11980 - 11980   2023.11

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    Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    DOI： 10.3390/app132111980

  • Effects of complexity enhancements on the search performance of multiobjective fuzzy genetics-based machine learning Reviewed

    T. Konishi, N. Masuyama, and Y. Nojima

    Proc. of the 20th World Congress of the International Fuzzy Systems Association   38 - 45   2023.08

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

  • A decomposition-based multi-modal multi-objective evolutionary algorithm with problem transformation into two-objective subproblems Reviewed International coauthorship

    Y. Nojima, Y. Fujii, N. Masuyama, Y. Liu, and H. Ishibuchi

    GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion   399 - 402   2023.07（ ISBN:9798400701207 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1145/3583133.3593950

  • Multi-label classification via adaptive resonance theory-based clustering Reviewed International coauthorship

    N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi

    IEEE Transactions on Pattern Analysis and Machine Intelligence   45 ( 7 )   8696 - 8712   2023.07（ ISSN:01628828 ）

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    Authorship：Lead author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    DOI： 10.1109/TPAMI.2022.3230414

  • System design optimization with mixed subsystems failure dependencies. Reviewed International coauthorship

    M. A. Mellal, E. Zio, S. Al-Dahidi, N. Masuyama, and Y. Nojima

    Reliability Engineering and System Safety   231   109005 - 109005   2023.03（ ISSN:09518320 ）

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    Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    DOI： 10.1016/j.ress.2022.109005

  • Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting Reviewed International coauthorship

    Z. Liu, G. A. Tahir, N. Masuyama, H. A. Kakudi, Z. Fu, and K. Pasupa

    Engineering Applications of Artificial Intelligence   117   2023.01（ ISSN:09521976 ）

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

    DOI： 10.1016/j.engappai.2022.105611

  • Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-Based Clustering for Many-Objective Optimization Reviewed

    T. Kinoshita, N. Masuyama, Y. Liu, Y. Nojima

    11   126066 - 126086   2023

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    Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    DOI： 10.1109/ACCESS.2023.3331747

  • Fuzzy Classifiers with a Two-Stage Reject Option Reviewed

    Y. Nojima, K. Kawano, H. Shimahara, E. Vernon, N. Masuyama, and H. Ishibuchi

    IEEE International Conference on Fuzzy Systems   2023（ ISSN:10987584 ） （ ISBN:9798350332285 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/FUZZ52849.2023.10309729

  • Adaptive Resonance Theory-Based Topological Clustering with Node Deletion Mechanism for Evolving Stream Data Reviewed

    T. Takebayashi, N. Masuyama, Y. Nojima

    Proc. of the International Conference on Machine Learning and Cybernetics   576 - 581   2023（ ISSN:2160133X ） （ ISBN:9798350303780 ）

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    Authorship：Corresponding author   Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/ICMLC58545.2023.10328009

  • Search process analysis of multiobjective evolutionary algorithms using convergence-diversity diagram Reviewed

    T. Kinoshita, N. Masuyama, and Y. Nojima

    Proc. of 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022   1 - 6   2022.12（ ISBN:9781665499248 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/SCISISIS55246.2022.10001961

    Other URL： https://dblp.uni-trier.de/db/conf/scisisis/scisisis2022.html#KinoshitaMN22

  • Composition-designed high entropy perovskite oxides for oxygen evolution catalysis Reviewed

    Y. Okazaki, Y. Fujita, H. Murata, I. Yamada, N. Masuyama, Y. Nojima, H. Ikeno, and S. Yagi

    Chemistry of Materials   34 ( 24 )   10973 - 10981   2022.12（ ISSN:0897-4756 ） （ eISSN:1520-5002 ）

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    Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    Oxygen evolution reaction (OER) catalysts play an essential role in energy-conversion electrochemical reactions. High-entropy oxides (HEOs) were recently investigated as promising candidates to realize highly active and cost-effective OER catalysts. Since the vast composition space for the HEOs needs considerable efforts to find promising catalysts, the further development beyond simple chemical compositions like equimolar ones has not been achieved yet. In this study, we conducted the fast and efficient design of the perovskite of La(Cr, Mn, Fe, Co, Ni)O3 with high OER catalytic activity using Bayesian optimization and found the relationship between chemical compositions and OER catalytic activities. The multielement perovskites with the optimized compositions exhibited much higher activities than the equimolar LaCr1/5Mn1/5Fe1/5Co1/5Ni1/5O3, which was previously reported as an active catalyst. Bayesian optimization adjusted the concentrations of OER active elements of Fe, Co, and Ni in high contents to enhance the catalytic activities. The optimization also indicates that the OER inactive elements (Cr and Mn) in perovskites even promote the OER activities. These findings suggest the solution of data-based predictions to improve catalytic performances in multielement transition-metal oxides.

    DOI： 10.1021/acs.chemmater.2c02986

  • Effects of complexity enhancements on the search performance of multiobjective fuzzy genetics-based machine learning Reviewed

    T. Konishi, N. Masuyama, and Y. Nojima

    Proc. of 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022   38 - 45   2022.08（ ISBN:9781665499248 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/SCISISIS55246.2022.10002139

    Other URL： https://dblp.uni-trier.de/db/conf/scisisis/scisisis2022.html#KonishiMN22

  • Evolutionary multiobjective multi-tasking for fuzzy genetics-based machine learning in multi-label classification Reviewed International coauthorship

    Y. Omozaki, N. Masuyama, Y. Nojima, and H. Ishibuchi

    Proc. of IEEE International Conference on Fuzzy Systems   2022-July   1 - 8   2022.07（ ISSN:10987584 ） （ ISBN:9781665467100 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/FUZZ-IEEE55066.2022.9882681

    Other URL： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2022.html#OmozakiMNI22

  • Analytical methods to separately evaluate convergence and diversity for multi-objective optimization Reviewed International coauthorship

    T. Kinoshita, N. Masuyama, Y. Nojima, and H. Ishibuchi

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   13838 LNCS   172 - 186   2022.07（ ISSN:03029743 ） （ ISBN:9783031265037 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1007/978-3-031-26504-4_13

    Other URL： https://dblp.uni-trier.de/db/conf/metaheuristics/metaheuristics2022.html#KinoshitaMNI22

  • Adaptive resonance theory-based clustering for handling mixed data Reviewed International coauthorship

    N. Masuyama, Y. Nojima, H. Ishibuchi, and Z. Liu

    Proc. of 2022 International Joint Conference on Neural Networks   2022-July   1 - 8   2022.07（ ISBN:9781728186719 ）

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    Authorship：Lead author   Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/IJCNN55064.2022.9892060

    Other URL： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2022.html#MasuyamaNIL22

  • Adaptive resonance theory-based clustering with a divisive hierarchical structure capable of continual learning Reviewed International coauthorship

    N. Masuyama, N. Amako, Y. Yamada, Y. Nojima, and H. Ishibuch

    IEEE Access   10   68042 - 68056   2022.06

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    Authorship：Lead author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

    DOI： 10.1109/ACCESS.2022.3186479

  • Effects of different optimization formulations in evolutionary reinforcement learning on diverse behavior generation Reviewed

    V. Villin, N. Masuyama, and Y. Nojima

    Proc. of 2021 IEEE Symposium Series on Computational Intelligence   1 - 8   2021.12（ ISBN:9781728190488 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/SSCI50451.2021.9659949

    Other URL： https://dblp.uni-trier.de/db/conf/ssci/ssci2021.html#VillinMN21

  • Multi-modal multi-objective traveling salesman problem and its evolutionary optimizer Reviewed International coauthorship

    Y. Liu, L. Xu, Y. Han, N. Masuyama, Y. Nojima, H. Ishibuchi, and G. G. Yen

    Proc. of 2021 IEEE International Conference on Systems, Man, and Cybernetics   770 - 777   2021.10（ ISBN:9781665442077 ）

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    Publishing type：Research paper (international conference proceedings)   International / domestic magazine：International journal  

    DOI： 10.1109/SMC52423.2021.9658818

    Other URL： https://dblp.uni-trier.de/db/conf/smc/smc2021.html#LiuXHMNIY21

  • クラス別FTCAに基づく識別器設計 Reviewed

    増山直輝, 坪田一希, 能島裕介, 石渕久生

    知能と情報（日本知能情報ファジィ学会誌）   33 ( 1 )   543 - 548   2021.02

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

  • クラス不均衡データに対するミシガン型ファジィ遺伝的機械学習 Reviewed International coauthorship

    西原光洋, 増山直輝, 能島裕介, 石渕久生

    知能と情報（日本知能情報ファジィ学会誌）   33 ( 1 )   537 - 542   2021.02

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

  • マルチラベル多目的ファジィ遺伝的機械学習の多数目的最適化への拡張 Reviewed International coauthorship

    面崎祐一, 増山直輝, 能島裕介, 石渕久生

    知能と情報（日本知能情報ファジィ学会誌）   33 ( 1 )   531 - 536   2021.02

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

  • 2目的問題に変換する分解ベース進化型マルチモーダル多目的最適化アルゴリズム Reviewed International coauthorship

    藤井祐人, 増山直輝, 能島裕介, 石渕久生

    知能と情報（日本知能情報ファジィ学会誌）   33 ( 1 )   537 - 542   2021.02

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

  • Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts Reviewed International coauthorship

    Y. Liu, H. Ishibuchi, N. Masuyama, and Y. Nojima

    IEEE Transactions on Evolutionary Computation   24 ( 3 )   439 - 453   2020.06

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

  • Handling imbalance between convergence and diversity in the decision space in evolutionary multi-modal multi-objective optimization Reviewed International coauthorship

    Y. Liu, H. Ishibuchi, G. G. Yen,Y. Nojima, and N. Masuyama

    IEEE Transactions on Evolutionary Computation   24 ( 3 )   551 - 565   2020.06

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    Publishing type：Research paper (scientific journal)   Kind of work：Joint Work   International / domestic magazine：International journal  

  • Many-Objective Problems Are Not Always Difficult for Pareto Dominance-Based Evolutionary Algorithms. Reviewed International coauthorship

    Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima

    ECAI 2020 - 24th European Conference on Artificial Intelligence(ECAI)   291 - 298   2020（ ISBN:9781643681009 ）

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    Publishing type：Research paper (international conference proceedings)   Kind of work：Joint Work   International / domestic magazine：International journal  

    DOI： 10.3233/FAIA200105

    Other URL： https://dblp.uni-trier.de/db/conf/ecai/ecai2020.html#IshibuchiMMN20

  • Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts. Reviewed

    Yiping Liu, Hisao Ishibuchi, Naoki Masuyama, Yusuke Nojima

    IEEE Transactions on Evolutionary Computation   24 ( 3 )   439 - 453   2020

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/TEVC.2019.2926151

  • On the Normalization in Evolutionary Multi-Modal Multi-Objective Optimization. Reviewed

    Yiping Liu, Hisao Ishibuchi, Gary G. Yen, Yusuke Nojima, Naoki Masuyama, Yuyan Han

    IEEE Congress on Evolutionary Computation(CEC)   1 - 8   2020（ ISBN:9781728169293 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CEC48606.2020.9185899

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2020.html#LiuIYNMH20

  • Multiobjective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification. Reviewed

    Yuichi Omozaki, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi

    29th IEEE International Conference on Fuzzy Systems(FUZZ-IEEE)   1 - 8   2020（ ISBN:9781728169323 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/FUZZ48607.2020.9177804

    Other URL： https://dblp.uni-trier.de/db/conf/fuzzIEEE/fuzzIEEE2020.html#OmozakiMNI20

  • Multilayer Clustering Based on Adaptive Resonance Theory for Noisy Environments. Reviewed

    Narito Amako, Naoki Masuyama, Chu Kiong Loo, Yusuke Nojima, Yiping Liu, Hisao Ishibuchi

    2020 International Joint Conference on Neural Networks(IJCNN)   1 - 8   2020（ ISBN:9781728169262 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/IJCNN48605.2020.9207071

    Other URL： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#AmakoMLNLI20

  • Multi-label Classification Based on Adaptive Resonance Theory. Reviewed

    Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi

    2020 IEEE Symposium Series on Computational Intelligence(SSCI)   1913 - 1920   2020（ ISBN:9781728125473 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/SSCI47803.2020.9308356

    Other URL： https://dblp.uni-trier.de/db/conf/ssci/ssci2020.html#MasuyamaNLI20

  • Handling Imbalance Between Convergence and Diversity in the Decision Space in Evolutionary Multimodal Multiobjective Optimization. Reviewed

    Yiping Liu, Hisao Ishibuchi, Gary G. Yen, Yusuke Nojima, Naoki Masuyama

    IEEE Transactions on Evolutionary Computation   24 ( 3 )   551 - 565   2020

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/TEVC.2019.2938557

  • Effects of Local Mating in Inter-task Crossover on the Performance of Decomposition-based Evolutionary Multiobjective Multitask optimization Algorithms. Reviewed

    Ryuichi Hashimoto, Toshiki Urita, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi

    IEEE Congress on Evolutionary Computation(CEC)   1 - 8   2020（ ISBN:9781728169293 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CEC48606.2020.9185871

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2020.html#HashimotoUMNI20

  • Effects of dominance resistant solutions on the performance of evolutionary multi-objective and many-objective algorithms. Reviewed

    Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima

    GECCO '20: Genetic and Evolutionary Computation Conference(GECCO)   507 - 515   2020（ ISBN:9781450371285 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1145/3377930.3390166

    Other URL： https://dblp.uni-trier.de/db/conf/gecco/gecco2020.html#IshibuchiMMN20

  • Divisive Hierarchical Clustering Based on Adaptive Resonance Theory.

    Yuna Yamada, Naoki Masuyama, Narito Amako, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi

    International Symposium on Community-centric Systems(CcS)   1 - 6   2020（ ISBN:9781728187419 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CcS49175.2020.9231474

    Other URL： https://dblp.uni-trier.de/db/conf/ccs3/ccs2020.html#YamadaMANLI20

  • 未知クラスの継続的な学習を可能とするファジィ遺伝的機械学習手法 Reviewed

    入江勇斗，増山直輝，能島裕介，石渕久生

    知能と情報 雑誌 日本知能情報ファジィ学会   2019.11

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    Kind of work：Joint Work  

  • 進化型多目的マルチタスク最適化手法におけるタスク間交叉時の親個体が探索性能に与える影響

    橋本龍一，増山直輝，能島裕介，石渕久生

    知能と情報 雑誌 日本知能情報ファジィ学会   2019.11

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    Kind of work：Joint Work  

  • Topological Clustering via Adaptive Resonance Theory with Information Theoretic Learning Reviewed

    Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota, Yusuke Nojima, and Yiping Liu

    IEEE Access 雑誌   7 ( 1 )   76920 - 76936   2019.06

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    Kind of work：Joint Work  

  • Topological Clustering via Adaptive Resonance Theory with Information Theoretic Learning Reviewed

    Naoki Masuyama, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota, Yusuke Nojima, Yiping Liu

    IEEE Access   7 ( 1 )   76920 - 76936   2019.06

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1109/ACCESS.2019.2921832

  • A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure Reviewed

    Naoki Masuyama, Chu Kiong Loo, and Stefan Wermter

    International Journal of Neural Systems 雑誌   29 ( 5 )   2019.01

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    Kind of work：Joint Work  

  • A Kernel Bayesian Adaptive Resonance Theory with a Topological Structure Reviewed

    Naoki Masuyama, Chu Kiong Loo, Stefan Wermter

    International Journal of Neural Systems   29 ( 5 )   1850052-1 - 1850052-20   2019.01

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1142/S0129065718500521

  • Two-Layered Weight Vector Specification in Decomposition-Based Multi-Objective Algorithms for Many-Objective Optimization Problems. Reviewed

    Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima

    IEEE Congress on Evolutionary Computation(CEC)   2434 - 2441   2019（ ISBN:9781728121536 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CEC.2019.8790344

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2019.html#IshibuchiIMN19

  • Optimal Distributions of Solutions for Hypervolume Maximization on Triangular and Inverted Triangular Pareto Fronts of Four-Objective Problems. Reviewed

    Hisao Ishibuchi, Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima

    IEEE Symposium Series on Computational Intelligence(SSCI)   1857 - 1864   2019（ ISBN:9781728124858 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/SSCI44817.2019.9003032

    Other URL： https://dblp.uni-trier.de/db/conf/ssci/ssci2019.html#IshibuchiMMN19

  • Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric. Reviewed

    Naoki Masuyama, Narito Amako, Yusuke Nojima, Yiping Liu, Chu Kiong Loo, Hisao Ishibuchi

    IEEE Symposium Series on Computational Intelligence(SSCI)   2215 - 2221   2019（ ISBN:9781728124858 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/SSCI44817.2019.9003098

    Other URL： https://dblp.uni-trier.de/db/conf/ssci/ssci2019.html#MasuyamaANLLI19

  • Effect of Solution Information Sharing between Tasks on the Search Ability of Evolutionary Multiobjective Multitasking Algorithms. Reviewed

    Ryuichi Hashimoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi

    IEEE Symposium Series on Computational Intelligence(SSCI)   2671 - 2678   2019（ ISBN:9781728124858 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/SSCI44817.2019.9002984

    Other URL： https://dblp.uni-trier.de/db/conf/ssci/ssci2019.html#HashimotoMNI19

  • Constrained multiobjective distance minimization problems. Reviewed

    Yusuke Nojima, Takafumi Fukase, Yiping Liu, Naoki Masuyama, Hisao Ishibuchi

    Proceedings of the Genetic and Evolutionary Computation Conference(GECCO)   586 - 594   2019（ ISBN:9781450361118 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1145/3321707.3321878

    Other URL： https://dblp.uni-trier.de/db/conf/gecco/gecco2019.html#NojimaFLMI19

  • Comparison of Hypervolume, IGD and IGD+ from the Viewpoint of Optimal Distributions of Solutions. Reviewed

    Hisao Ishibuchi, Ryo Imada, Naoki Masuyama, Yusuke Nojima

    Evolutionary Multi-Criterion Optimization - 10th International Conference(EMO)   332 - 345   2019（ ISBN:9783030125974 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1007/978-3-030-12598-1_27

    Other URL： https://dblp.uni-trier.de/db/conf/emo/emo2019.html#IshibuchiIMN19

  • A Multiobjective Test Suite with Hexagon Pareto Fronts and Various Feasible Regions. Reviewed

    Takashi Matsumoto, Naoki Masuyama, Yusuke Nojima, Hisao Ishibuchi

    IEEE Congress on Evolutionary Computation(CEC)   2058 - 2065   2019（ ISBN:9781728121536 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CEC.2019.8790277

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2019.html#MatsumotoMNI19

  • Searching for Local Pareto Optimal Solutions: A Case Study on Polygon-Based Problems. Reviewed

    Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Yuyan Han

    IEEE Congress on Evolutionary Computation(CEC)   896 - 903   2019（ ISBN:9781728121536 ）

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    Publishing type：Research paper (international conference proceedings)  

    DOI： 10.1109/CEC.2019.8790066

    Other URL： https://dblp.uni-trier.de/db/conf/cec/cec2019.html#LiuINMH19

  • Effects of the Number of Constraints on the Performance of Multi-Objective Evolutionary Algorithms Reviewed

    [6] Yuki Tanigaki, Naoki Masuyama, and Yusuke Nojima

    International Journal of Computer Science and Network Security   18 ( 12 )   221 - 231   2018.12

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    Kind of work：Joint Work  

  • Diagnosing Metabolic Syndrome using Genetically Optimised Bayesian ARTMAP Reviewed

    Habeebah Adamu Kakudi, Chu Kiong Loo, Foong Ming Moy, and Naoki Masuyama

    IEEE Access 雑誌   7 ( 1 )   8437 - 8453   2018.11

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    Kind of work：Joint Work  

  • Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction Reviewed

    Zongying Liu, Chu Kiong Loo, Naoki Masuyama, and Kitsuchart Pasupa

    IEEE Access 雑誌   6   19583 - 19596   2018.04

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    Kind of work：Joint Work  

  • Kernel Bayesian ART and ARTMAP Reviewed

    Naoki Masuyama, Chu Kiong Loo, and Farhan Dawood

    Neural Networks 雑誌   98   76 - 86   2017.11

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    Kind of work：Joint Work  

  • Personality affected robotic emotional model with associative memory for human-robot interaction Reviewed

    Naoki Masuyama, Chu Kiong Loo, and Manjeevan Seera

    Neurocomputing 雑誌   272   213 - 225   2017.07

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    Kind of work：Joint Work  

  • Quantum-Inspired Multidirectional Associative Memory with a Self-Convergent Iterative Learning Reviewed

    Naoki Masuyama, Chu Kiong Loo, Manjeevan Seera, and Naoyuki Kubota

    IEEE Transactions on Neural Networks and Learning Systems 雑誌   29 ( 4 )   1058 - 1068   2017.02

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    Kind of work：Joint Work  

  • Application of Emotion Affected Associative Memory based on Mood Congruency Effects for a Humanoid Reviewed

    Naoki Masuyama, Md. Nazrul Islam, Manjeevan Seera, and Chu Kiong Loo

    Neural Computing and Applications 雑誌   28 ( 4 )   737 - 752   2015.11

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    Kind of work：Joint Work  

  • Quantum-Inspired Bidirectional Associative Memory for Human-Robot Communication Reviewed

    Naoki Masuyama, Chu Kiong Loo, and Naoyuki Kubota

    International Journal of Humanoid Robotics 雑誌   11 ( 2 )   2014.05

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    Kind of work：Joint Work  

▼display all

Books and Other Publications

  • Fast Multi-scale Batch-Learning Growing Neural Gas

    T. Obo, N.Kubota, Y. Toda, N. Masuyama（ Role： Joint author）

    2024.07  （ ISBN:9783031582578 ）

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    Recently, various types of unsupervised learning methods have been applied to data mining tasks. The main objectives of unsupervised learning are feature extraction, clustering, and the topological mapping of a dataset to find important information efficiently. In general, a topology is represented by the set of nodes and edges. For example, Growing Neural Gas (GNG) can obtain a topological structure by connecting an edge between the first and second nearest nodes with each sample data. Furthermore, Growing When Required (GWR), batch-learning GNG (BL-GNG), multi-scale BL-GNG (MS-BL-GNG), and others have been proposed to improve the learning speed and convergence property. In the above methods, we need many data sampling times sufficient to conduct the clustering and topological mapping simultaneously. However, it is difficult for standard GNG to enhance the learning speed drastically because a node is added to a current network after errors with sampling data are accumulated many times. Therefore, we have proposed new growing methods to enhance the learning speed of MS-BL-GNG drastically. In this method, a sample data is added as a new node directly to a current network according to the node addition probability calculated by the distance with the third nearest node in addition to the first and second nearest nodes at maximal. Based on this idea, we have proposed the overall methodology of multi-scale batch-leaning from the viewpoints of learning and growing procedures, that is called Fast GNG in short. In this paper, we discuss the effectiveness of Fast GNG through benchmark comparison. Furthermore, we discuss the future research direction of Fast GNG.

    DOI： 10.1007/978-3-031-58257-8_2

MISC

  • 閾値に基づく棄却オプションを導入したファジィ識別器における閾値最適化手法の検討

    島原基, 増山直輝, 能島裕介

    第40回ファジィシステムシンポジウム講演論文集   2024.09

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    Publishing type：Research paper, summary (national, other academic conference)  

  • 継続学習におけるリプレイバッファのためのデータ選択手法の比較検討

    藤井亮輔, 増山直輝, 能島裕介

    第40回ファジィシステムシンポジウム講演論文集   2024.09

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    Publishing type：Research paper, summary (national, other academic conference)  

  • 継続学習におけるリプレイバッファに保持するデータの選択手法の調査

    藤井亮輔, 増山直輝, 能島裕介

    第7回継続学習と知能の創発研究会講演論文集   2024.06

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    Authorship：Corresponding author   Publishing type：Research paper, summary (national, other academic conference)  

Presentations

  • Adaptive Resonance Theory-based Topological Clustering and its Applications Invited International conference

    N. Masuyama

    2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems  2024.10  2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems

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    Presentation type：Oral presentation (invited, special)  

  • 機械学習セミナー「クラスタリングの基礎」 Invited

    増山直輝

    2024.09  日立造船株式会社

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    Presentation type：Public lecture, seminar, tutorial, course, or other speech  

  • アーカイブ個体群を用いた2段階ファジィ遺伝的機械学習の検討 Domestic conference

    小西豪，増山直輝，能島裕介

    第39回ファジィシステムシンポジウム講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

  • 応共鳴理論に基づく階層的トポロジカルクラスタリングにおけるクラスタリング性能向上方法の検討 Domestic conference

    鳥越大貴，田代一貴，増山直輝，能島裕介，伊藤諒適，三宅寿英，馬野元秀

    第39回ファジィシステムシンポジウム講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

  • ε-局所差分プライバシを考慮した適応共鳴理論に基づく連合クラスタリング手法の検討 Domestic conference

    上田裕也，増山直輝，能島裕介

    第39回ファジィシステムシンポジウム講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

  • 制約付き問題のための適応的問題分割ベース進化型多目的最適化アルゴリズムの検討 Domestic conference

    木下貴登，増山直輝，能島裕介

    第39回ファジィシステムシンポジウム講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

  • Overview of techniques for rule extraction from neural networks Domestic conference

    ベーノンエリック，増山直輝，能島裕介

    第39回ファジィシステムシンポジウム講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

  • 実世界多目的最適化問題のためのRiesz discrete s-Energy によるConvergence-Diversity Diagramの拡張 Domestic conference

    木下貴登，増山直輝，能島裕介

    第24回進化計算研究会講演論文集  2023.09 

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    Presentation type：Oral presentation (general)  

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Outline of collaborative research (seeds)

  • クラスタリングによるデータマイニング関連

Grant-in-Aid for Scientific Research

  • トポロジカルクラスタリングによる位相幾何学的情報の継続的グラフ構築とその応用

    Grant-in-Aid for Scientific Research(B)  2027

  • トポロジカルクラスタリングによる位相幾何学的情報の継続的グラフ構築とその応用

    Grant-in-Aid for Scientific Research(B)  2026

  • トポロジカルクラスタリングによる位相幾何学的情報の継続的グラフ構築とその応用

    Grant-in-Aid for Scientific Research(B)  2025

  • Development of Evolutionary Multiobjective Optimization Algorithms and Benchmark Problem Design based on the Analysis of Real-world Problems

    Grant-in-Aid for Scientific Research(B)  2025

  • 継続的な知識の学習と忘却を両立する適応的クラスタリング手法の開発

    Grant-in-Aid for Scientific Research(C)  2024

  • Development of Evolutionary Multiobjective Optimization Algorithms and Benchmark Problem Design based on the Analysis of Real-world Problems

    Grant-in-Aid for Scientific Research(B)  2024

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Charge of on-campus class subject

  • 情報工学卒業研究A

    2025   Intensive lecture   Undergraduate

  • 情報工学技術英語

    2025   Weekly class   Undergraduate

  • 情報工学演習3

    2025   Weekly class   Undergraduate

  • 情報工学演習1

    2025   Weekly class   Undergraduate

  • 基幹情報学特別研究2

    2025   Intensive lecture   Graduate school

  • 基幹情報学特別研究1

    2025   Intensive lecture   Graduate school

  • 情報リテラシー

    2025   Weekly class   Undergraduate

  • 情報リテラシー

    2025   Weekly class   Undergraduate

  • 情報工学演習3

    2024   Weekly class   Undergraduate

  • 情報工学演習1

    2024   Weekly class   Undergraduate

  • 基幹情報学特別研究1

    2024   Intensive lecture   Graduate school

  • 基幹情報学特別研究2

    2024   Intensive lecture   Graduate school

  • 工学研究の最先端

    2024   Intensive lecture   Graduate school

  • 情報リテラシー

    2024   Weekly class   Graduate school

  • 情報リテラシー

    2024   Weekly class   Graduate school

  • 計算知能

    2024   Weekly class   Undergraduate

  • 意思決定理論

    2024   Weekly class   Undergraduate

  • 情報工学実験2

    2024   Weekly class   Undergraduate

  • 情報工学演習2

    2024   Weekly class   Undergraduate

  • 情報工学基礎演習2

    2024   Weekly class   Undergraduate

  • 基幹情報学特別演習2

    2024   Weekly class   Graduate school

  • 先端的計算知能

    2024   Weekly class   Graduate school

  • 基幹情報学特別研究2

    2024   Intensive lecture   Graduate school

  • 基幹情報学特別研究1

    2024   Intensive lecture   Graduate school

  • 計算知能特論

    2023   Weekly class  

  • 計算知能

    2023   Weekly class  

  • 意思決定理論

    2023   Weekly class  

  • 情報工学基礎演習2

    2023   Weekly class  

  • 情報工学演習II

    2023   Weekly class  

  • Practicum in Computer Science I

    2021    

  • Practicum in Computer Science III

    2021    

  • Fundamentals in Electrical and Electronic Engineering II

    2021    

  • Fundamentals in Electrical and Electronic Engineering II

    2021    

  • Decision Making Theory

    2021    

  • Practicum in Computer Science II

    2021    

  • Electrical, Electronic, and Information Engineering in Modern Society

    2021    

  • Computational Intelligence

    2021    

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Number of papers published by graduate students

  • 2023

    Number of undergraduate student / college student presentations：4  Number of graduate students presentations：7

Social Activities　⇒ Link to the list of Social Activities

  • 大阪府立天王寺高等学校スーパーサイエンスハイスクール（SSH）事業

    Role(s)： Consultant

    Type： Research consultation,　University open house,　Cooperation business with The administrative, educational institutions, etc.

    大阪府立天王寺高校SSH  大阪府生徒研究発表会（第1部）〜大阪サイエンスデイ〜  2024.10

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    SDGs：

  • テクノラボツアー

    Role(s)： Lecturer

    Type： Lecture

    大阪公立大学大学院 工学研究科  2023.09

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    SDGs：

  • 夢ナビライブ2021 Web in Autumn

    Role(s)： Lecturer

    Type： Lecture

    株式会社フロムページ  夢ナビライブ2021 Web in Autumn  2021.10

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    SDGs：

  • 模擬講義（西宮東高校）

    Role(s)： Lecturer

    Type： Visiting lecture

    大阪府立大学大学院工学研究科  模擬講義（西宮東高校）  2019.10

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    SDGs：