Abstract
XCSR is an accuracy-based learning classifier system 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 paper we investigate PCA-XCSR (a sequential application of PCA and XCSR) in three environments with different characteristics: a discrete and imbalanced environment (KDD’99 network intrusion), a continuous and highly symmetric environment (MiniBooNE), and a highly discrete, highly imbalanced environment (Census/Income (KDD)). These experiments show that in the three different environments, PCA-XCSR, in addition to being able to reduce the computational resources and time requirements of XCSR by approximately 50 %, is able to consistently maintain its high accuracy. In addition to that, it reduces the required population size needed by XCSR. Also, we suggest heuristics for selecting the number of principal components to use when using PCA-XCSR.
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The first author would like to acknowledge the financial support provided by the Robert and Maude Gledden Scholarship.
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Behdad, M., French, T., Barone, L. et al. On principal component analysis for high-dimensional XCSR. Evol. Intel. 5, 129–138 (2012). https://doi.org/10.1007/s12065-012-0075-6
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DOI: https://doi.org/10.1007/s12065-012-0075-6