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Publication - Dr Tim Kovacs

    Classifier generalization for comprehensive classifiers subsumption in XCS


    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 . Association for Computing Machinery (ACM), pp. 1854-1861


    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.

    Full details in the University publications repository