Approach to Clustering with Variance-Based XCS

• Zhang Caili

  The University of Electro-Communications

• Tatsumi Takato

  The University of Electro-Communications

• Nakata Masaya

  Yokohama National University

• Takadama Keiki

  The University of Electro-Communications

Abstract

This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a bench-mark problem to examine whether XCS-VRc can cluster both the generalized and more generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCS-VR.

Journal

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (5), 885-894, 2017-09-20

  Fuji Technology Press Ltd.

Keywords

• machine learning
• learning classifier system

Details

• CRID: 1390564238047793536
• NII Article ID: 130007520186
• NII Book ID: AA12042502
• DOI: 10.20965/jaciii.2017.p0885
• ISSN: 18838014
• ISSN: 13430130
• NDL BIB ID: 028510873
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028510873
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002100050014
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed