Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data
Hiroyasu Matsushima* and Keiki Takadama**
*The National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba Center 1, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan
**The University of Electro-Communication
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
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.
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