Updated on 2024/11/13

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
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

  • Emerging Research Leader Award

    Naoki Masuyama

    2024.11   Japan Society for Fuzzy Theory and Intelligent Informatics  

  • 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  

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

▼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

     More details

    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

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

     More details

    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

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    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 

     More details

    Presentation type:Oral presentation (general)  

▼display all

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

  • 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

Charge of on-campus class subject

  • 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2

    2024   Intensive lecture   Graduate school

  • 基幹情報学特別研究1

    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

  • 工学研究の最先端

    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    

▼display all

Number of papers published by graduate students

  • 2023

    Number of undergraduate student / college student presentations: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

     More details

    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

     More details

    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

     More details

    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

     More details

    SDGs: