Journal article

An enhanced XCS rule discovery module using feature ranking

Mani Abedini, Michael Kirley

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | SPRINGER HEIDELBERG | Published : 2013

Abstract

XCS is a genetics-based machine learning model that combines reinforcement learning with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. Like many other machine learning algorithms, XCS is less effective on high-dimensional data sets. In this paper, we describe a new guided rule discovery mechanisms for XCS, inspired by feature selection techniques commonly used in machine learning. In our approach, feature quality information is used to bias the evolutionary operators. A comprehensive set of experiments is used to investigate how the number of features used to bias the evolutionary operators, population size, and feature ranking technique..

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