Classifier generalization for comprehensive classifiers subsumption in XCS

Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

We proposed XCS-VRc3 that can extract useful rules (classifiers) from data and verify its effectiveness. The difficulty of mining real world data is that not only the type of the input state but also the number of instances varies. Although conventional method XCS-VRc is able to extract classifiers, the generalization of classifiers was insufficient and lack of human readability. The proposed XCS-VRc3 incorporating "generalization mechanism by comprehensive classifier subsumption" to solves this problem. Specifically, (1) All classifiers of the matching set subsume other classifiers, (2) Abolition of the inappropriate classifier deletion introduced by XCS-VRc (3) Preferentially select classifier with small variance of output in genetic algorithm. To verify the effectiveness of XCS-VRc3, we applied on care plan planning problem in a nursing home (in this case, identifying daytime behavior contributing to increase the ratio of deep sleep time). Comparing the association rules obtained by Apriori, and classifiers obtained by XCS-VRc3, the followings was found. First, abolishing the inappropriate classifier deletion and comprehensively subsuming promotes various degrees of generalization. Second, parent selection mechanism can obtain classifiers with small output variance. Finally, XCS-VRc3 is able to extract a small number classifiers equivalent to large number of rules found in Apriori.

Original languageEnglish
Title of host publicationGECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery (ACM)
Pages1854-1861
Number of pages8
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - 6 Jul 2018
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period15/07/1819/07/18

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Keywords

  • Data mining
  • LCS
  • XCS

Cite this

Zhang, C., Tatsumi, T., Sato, H., Kovacs, T., & Takadama, K. (2018). Classifier generalization for comprehensive classifiers subsumption in XCS. In GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1854-1861). Association for Computing Machinery (ACM). https://doi.org/10.1145/3205651.3208260