Extracting Different Abstracted Level Rule with Variance-Based LCS

Abstract

This paper extends the variance-based Learning Classifier System called XCS-SAC, in order to extract two different abstracted level rules (i.e.classifiers). Since XCS-SAC attempts to evolve classifiers whose generality depends on their own parameter, such an attempt results in generating many specific classifiers (i.e.the classifiers having a less number of #). Due to inappropriate generalization, some of classifiers might not be human-understandable. To overcome this problem, our LCS focuses on an extraction of only two different abstracted level rules, both the specific and general rules, to understand a tendency in a given problem. In detail, the specific rules can be only utilized in limited situations but they are very accurate, while the general rules can be widely utilized but they are not accurate. The experimental result shows that our LCS succeeds to extract both specific and general rules appropriately in comparison with XCS-SAC.