Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data

• Matsushima Hiroyasu

  The National Institute of Advanced Industrial Science and Technology (AIST)

• Takadama Keiki

  The University of Electro-Communication

Abstract

In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECS-DMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.

Journal

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (5), 868-875, 2017-09-20

  Fuji Technology Press Ltd.

Keywords

• learning classifier system
• exemplar
• knowledge extraction
• imbalanced data set
• single-step problems

Details

• CRID: 1390001288092321920
• NII Article ID: 130007520189
• NII Book ID: AA12042502
• DOI: 10.20965/jaciii.2017.p0868
• ISSN: 18838014
• ISSN: 13430130
• NDL BIB ID: 028510841
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028510841
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002100050012
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed