Updated on 2024/04/17

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)

  • 会計担当   日本知能情報ファジィ学会関西支部  

    2023.04 - 2025.03 

  • 会計担当   日本知能情報ファジィ学会事業委員会  

    2023.04 - 2025.03 

  • 会計幹事   継続学習と知能の創発研究会  

    2021.04 - Now 

  • 事業委員   日本知能情報ファジィ学会  

    2019.04 - Now 

Awards

  • 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  

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

  • 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 ）

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    <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

  • 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 ）

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    <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

  • 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

  • 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

    Kinoshita T.

    IEEE Access   11   126066 - 126086   2023

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  • Fuzzy Classifiers with a Two-Stage Reject Option

    Nojima Y.

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

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

    Takebayashi T.

    Proceedings - International Conference on Machine Learning and Cybernetics   576 - 581   2023（ ISSN:2160133X ） （ ISBN:9798350303780 ）

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  • A Study on Improvement of Clustering Performance for Hierarchical Topological Clustering based on Adaptive Resonance Theory

    Torigoe Taiki, Tashiro Kazuki, Masuyama Naoki, Nojima Yusuke, Itoh Ryo, Miyake Toshihide, Umano Motohide

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   496 - 501   2023

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    <p>Hierarchical clustering can extract knowledge with various information granularities from data, and various methods have been studied. One of them is a hierarchical model of CIM-based ART with Edge and Age (CAEA) based on the Adaptive Resonance Theory (HCAEA). HCAEA automatically calculates a similarity threshold from the distribution of data points. However, it has the problem of excessive node generation. In this paper, the number of initial nodes generated in the second and subsequent layers is changed, and a new node generation criterion is introduced to suppress excessive node generation. Furthermore, we aim to improve the clustering performance by reusing the data discarded with the deletion of nodes in the training process of HCAEA. Numerical experiments on real-world data confirmed that the proposed method improves clustering performance.</p>

    DOI： 10.14864/fss.39.0_496

  • Multi-label Classification for Handling Mixed Data via Adaptive Resonance Theory-based Clustering

    Nishikawa Tsuyoshi, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   484 - 489   2023

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    <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 for the multi-label classification problems, called Multi-Label CIM-based ART (MLCA). MLCA adaptively and continually generates nodes corresponding to input data, and the generated nodes are used as a classifier. Many real-world multi-label classification problems are mixed datasets that contain both numerical and categorical attributes. In a mixed dataset, it is necessary to apply an appropriate similarity for each numerical and categorical attribute. This study extends MLCA to mixed datasets by measuring the similarity between data using correntropy for numerical attributes and hamming distance for categorical attributes. Numerical experiments on real-world datasets show the effectiveness of the proposed method.</p>

    DOI： 10.14864/fss.39.0_484

  • A Study of Adaptive Decomposition-based Multiobjective Evolutionary Algorithms for Solving Constrained Problems

    Kinoshita Takato, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   279 - 284   2023

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    <p>Many optimization tasks in the real world can be regarded as Multiobjective Optimization Problems (MOPs) that have multiple objectives to be optimized. Due to trade-off relationships among objectives, MOPs usually have the Pareto-optimal solution set (PS) instead of a single optimal solution. In addition, there is the Pareto-optimal front (PF) which is the image of the PS in the objective space. Decomposition-based Multiobjective Evolutionary Algorithms (MOEAs) are one of the most popular categories of algorithms for MOPs. In the previous study, we introduced CIM-based Adaptive resonance theory (CA), a topological clustering algorithm, into a decomposition-based MOEA to realize the adaptive decomposition according to the PF shape and proposed RVEA-CA. Although several studies show that adaptive decomposition-based MOEAs, including RVEA-CA, have high search performance on MOPs with a large number of objectives and high versatility on the various PF shapes, the effect of adaptive decomposition on constrained MOPs has not yet been investigated, to our knowledge. Hence, this paper introduces a constraint-handling method into RVEA-CA and investigates the search performance on constrained MOPs. The computational experiments showed that the proposed method has search performance equal to or better than those of four state-of-the-art constrained MOEAs and discussed the effectiveness of the adaptive decomposition on constrained MOPs.</p>

    DOI： 10.14864/fss.39.0_279

  • A Study on Two-Stage Multi-objective Fuzzy Genetics-based Machine Learning Using an Archive Population

    Konishi Takeru, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   666 - 671   2023

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    <p>Multi-objective fuzzy genetics-based machine learning (MoFGBML), one of the most well-known multi-objective evolutionary fuzzy systems, can efficiently obtain a set of fuzzy classifiers considering the maximization of classification performance and the minimization of the model complexity. However, MoFGBML has a strong bias towards minimizing complexity in the search process, which makes it easy to obtain classifiers with low complexity but difficult to obtain classifiers with high classification performance. As a result, the number of non-dominated classifiers is often small. 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. In this paper we consider the use of an archive population in two-stage fuzzy genetics-based machine learning to further increase the number of non-dominated solutions and improve the tradeoff curve between two objectives.</p>

    DOI： 10.14864/fss.39.0_666

  • A Study on Adaptive Resonance Theory-based Clustering Incorporating Federated Clustering with Local ε-Differential Privacy

    Ueda Yuya, Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   478 - 483   2023

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    <p>Federated learning is a machine learning method that merges and updates models obtained from multiple devices learning each dataset. Compared to conventional machine learning methods, federated learning is expected to significantly reduce the learning time by distributed computing on each device in parallel. In addition, federated learning combined with a data anonymization method, called differential privacy, can guarantee a high degree of privacy. In this paper, we propose two methods. First, in the case of federated learning with a clustering method called CIM-based ART (CA), which represent clusters by nodes, we propose a method of merging nodes by reapplying CA with the generated nodes as input data. Second, we propose a method that combines federated learning with CA and differential privacy as a method for learning while guaranteeing a high degree of privacy. Numerical experiments showed that federated learning with CA achieves faster learning while maintaining a similar degree of clustering performance compared to the conventional method of machine learning with CA. The clustering performance results for a real-world dataset trained with a method that combines federated learning with CA and differential privacy were used to investigate the effect of noise from differential privacy on clustering performance.</p>

    DOI： 10.14864/fss.39.0_478

  • Overview of Techniques for Rule Extraction From Neural Networks

    Vernon Eric M., Masuyama Naoki, Nojima Yusuke

    Proceedings of the Fuzzy System Symposium   39 ( 0 )   141 - 145   2023

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    <p>Neural networks have been a staple of the machine learning community since their inception decades ago. They have enjoyed a further surge in popularity as hardware and data limitations have allowed for the creation of deep neural networks, which have shown remarkable results in fields such as reinforcement learning and image classification, among others. However, the black box nature of neural networks presents a hurdle to their adoption, particularly in safety-critical domains such as medical diagnosis. A natural approach to explain the behavior of neural networks is the extraction of an interpretable set of rules which sufficiently mimic the behavior of the neural network. In this review, we provide an overview of the latest research in the field of rule extraction from neural networks. We also present a taxonomy for rule extraction techniques and highlight areas which we feel could be targeted for future research.</p>

    DOI： 10.14864/fss.39.0_141

  • 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 ）

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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

  • Accuracy-Rejection Tradeoff Analysis in Fuzzy Classifiers with a Two-Stage Reject Option International coauthorship

    Kawano Koyo, Vernon Eric, Masuyama Naoki, Nojima Yusuke, Ishibuchi Hisao

    Proceedings of the Fuzzy System Symposium   38 ( 0 )   250 - 255   2022.09

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    Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

    <p>In general, fuzzy classifiers have high interpretability because fuzzy classifiers can linguistically explain the classification reason by fuzzy sets used in the antecedent conditions of rules. A reject option that rejects patterns near the boundaries between different classes is an approach to increase the reliability of fuzzy classifiers. However, the conventional threshold-based reject option may reject more patterns than necessary to achieve high reliability. In this paper, we propose a two-stage reject option where after the threshold-based decision, the k-nearest neighbor is used for patterns with low confidence value than the threshold. If the class labels predicted by the k-nearest neighbor and the fuzzy classifier are the same, the fuzzy classifier outputs the predicted class label without rejection. Through computational experiments, we discuss the relationship between accuracy and the rejection rate.</p>

    DOI： 10.14864/fss.38.0_250

  • Comparison of Different Hierarchical Implementations in Topological Clustering with a Hierarchical Structure International coauthorship

    Tashiro Kazuki, Masuyama Naoki, Nojima Yusuke, Ishibuchi Hisao

    Proceedings of the Fuzzy System Symposium   38 ( 0 )   702 - 707   2022.09

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

    <p>A topological clustering algorithm can adaptively generate networks consisting of nodes and edges to approximate the data distribution. In our previous study, we proposed a topological clustering algorithm with a hierarchical structure to extract a hierarchical structure of the entire data while performing clustering. However, this algorithm does not utilize the information of clusters because each node holds the data that contributed to the creation of the node, and the data is independently used as the training data for the next layer. For improving clustering performance, this paper proposes an approach to aggregate the data that contributed to the creation of nodes in the same cluster and to utilize the aggregated data as training data for the next layer. Based on experimental results using artificial and real-world datasets, the characteristics of the proposed algorithm are discussed.</p>

    DOI： 10.14864/fss.38.0_702

  • Multiobjective Fuzzy Genetics-based Machine Learning with Accuracy-Oriented Pre-Optimization

    Konishi Takeru, Masuyama Naoki, Nojima Yusuke, Ishibuchi Hisao

    Proceedings of the Fuzzy System Symposium   38 ( 0 )   628 - 633   2022.09

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    International / domestic magazine：Domestic journal  

    <p>Fuzzy classifier design requires maximization of classification accuracy and minimization of complexity. Multiobjective Fuzzy Genetics-Based Machine Learning (MoFGBL) can efficiently obtain a set of fuzzy classifiers considering the above-mentioned two objectives at the same time using an evolutionary multiobjective optimization algorithm. However, the search by MoFGBML is biased to minimize the complexity, and it is easy to obtain classifiers with low complexity. At the same time, it is difficult to obtain classifiers with high classification accuracy. In this paper, we propose a two-stage MoFGBML, which first performs accuracy-oriented single-objective optimization to obtain a set of accurate classifiers with a large number of rules. Then, multiobjective optimization is performed to obtain a wide variety of classifiers, from highly-accurate ones to simple ones.</p>

    DOI： 10.14864/fss.38.0_628

  • Evolutionary Design Optimization of Economic Support Policies by Social Simulations International coauthorship

    Nakagawa Yuto, Kinoshita Takato, Masuyama Naoki, Nojima Yusuke, Ishibuchi Hisao

    Proceedings of the Fuzzy System Symposium   38 ( 0 )   326 - 329   2022.09

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

    <p>In recent years, severe economic damages have been caused by the spread of the Covid-19 infection. The design of economic support policies using social simulations has been paid great attention. In the Evolutionary Computation Competition 2021, a multiobjective optimization problem was posed to design economic support policies that simultaneously optimize the elimination of impoverished conditions and an appropriate level of payments. Economic support policies are evaluated using a social simulation with synthetic population data and multiple economic shock scenarios. In this paper, we apply an evolutionary multiobjective optimization algorithm to the design of promising economic support policies. We analyze the characteristics of Pareto optimal economic support policies and the structure of the multiobjective optimization problem through computational experiments.</p>

    DOI： 10.14864/fss.38.0_326

  • Incorporating Fairness Measures into Multiobjective Fuzzy Genetics-based Machine Learning International coauthorship

    Hiroki Nishiura, Masuyama Naoki, Nojima Yusuke, Ishibuchi Hisao

    Proceedings of the Fuzzy System Symposium   38 ( 0 )   256 - 261   2022.09

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    Publishing type：Research paper (conference, symposium, etc.)   Kind of work：Joint Work   International / domestic magazine：Domestic journal  

    <p>In recent years, pattern classification has been used for various real-world problems. However, there may be a bias toward certain attributes in data collection. This may result in inappropriate classification biased toward particular social groups. For example, when designing a classifier that recommends whether an applicant should be hired or not for recruitment problems, there is a possibility that attributes such as race and gender affect hiring outcomes. So far, we have developed multiobjective fuzzy genetics-based machine learning (MoFGBML) considering classification performance and interpretability. This paper incorporates two fairness measures into MoFGBML to design fuzzy classifiers considering classification performance, interpretability, and fairness.</p>

    DOI： 10.14864/fss.38.0_256

  • 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

  • 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

  • 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 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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  • 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  

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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

Charge of on-campus class subject

  • 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    

▼display all