Guided Rule Discovery in XCS for High-Dimensional Classification Problems

  • Mani Abedini
  • Michael Kirley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. In this paper, we investigate the effectiveness of XCS in high-dimensional classification problems where the number of features greatly exceeds the number of data instances – common characteristics of microarray gene expression classification tasks. We introduce a new guided rule discovery mechanisms for XCS, inspired by feature selection techniques commonly used in machine learning. The extracted feature quality information is used to bias the evolutionary operators. The performance of the proposed model is compared with the standard XCS model and a number of well-known machine learning algorithms using benchmark binary classification tasks and gene expression data sets. Experimental results suggests that the guided rule discovery mechanism is computationally efficient, and promotes the evolution of more accurate solutions. The proposed model performs significantly better than comparative algorithms when tackling high-dimensional classification problems.

Keywords

Information Gain Evolutionary Operator Feature Selection Technique Uniform Crossover Rule Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mani Abedini
    • 1
  • Michael Kirley
    • 1
  1. 1.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia

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