PCA for improving the performance of XCSR in classification of high-dimensional problems

XCSR is an accuracy-based learning classifier system (LCS) which can handle classification problems with real-value features. However, as the number of features increases, a high classification accuracy comes at the cost of more resources: larger population sizes and longer computational running times. In this research, we present a PCA-enhanced LCS, which uses principal component analysis (PCA) as a preprocessing step for XCSR, and examine how it performs on complex multi-dimensional real-world data. The experiments show that this technique, in addition to significantly reducing the computational resources and time requirements of XCSR, maintains its high accuracy and even occasionally improves it. In addition to that, it reduces the required population size needed by XCSR.

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