許容誤差を自己適応可能な学習分類子システム

抄録

The XCS classifier system is designed to evolve accurately generalized classifiers as an optimal solution to a problem. All classifiers are identified as either accurate or inaccurate on the basis of a pre-defined parameter called an accuracy criterion. Previous results suggested a standard setting of the accuracy criterion robustly performs on multiple simple problems so XCS evolves the optimal solution. However, there lacks a guideline of reasonable setting of accuracy criterion. This causes a problem that the accuracy criterion should be empirically customized for each complex problems especially noisy problems which is a main focus of this paper. This paper proposes a self-adaptation technique for the accuracy criterion which attempts to enable XCS to evolve the optimal solution on the noisy problems. In XCS-SAC(XCS with Self-Adaptive accuracy criterion), each classifier has its own accuracy criterion in order to find an adequate setting of accuracy criterion for each niche. Then, each classifier's accuracy criterion is updated with the variance of reward which its classifier has received. We test XCS-SAC on a benchmark classification problem (i.e., the multiplexer problem) with noise (the Gaussian noise and alternative noise). Experimental results show XCS-SAC successfully solves the noisy multiplexer problems as well as XCS but evolves a more compact solution including an optimal solution than XCS.