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Guided rule discovery in XCS for high-dimensional classification problems

Published: 05 December 2011 Publication History

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.

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Cited By

View all
  • (2019)Implications of the curse of dimensionality for supervised learning classifier systemsPattern Analysis & Applications10.1007/s10044-017-0649-022:2(519-536)Online publication date: 25-May-2019
  • (2018)XCSR based on compressed input by deep neural network for high dimensional dataProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208281(1418-1425)Online publication date: 6-Jul-2018

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Information

Published In

cover image Guide Proceedings
AI'11: Proceedings of the 24th international conference on Advances in Artificial Intelligence
December 2011
821 pages
ISBN:9783642258312
  • Editors:
  • Dianhui Wang,
  • Mark Reynolds

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 December 2011

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Cited By

View all
  • (2019)Implications of the curse of dimensionality for supervised learning classifier systemsPattern Analysis & Applications10.1007/s10044-017-0649-022:2(519-536)Online publication date: 25-May-2019
  • (2018)XCSR based on compressed input by deep neural network for high dimensional dataProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208281(1418-1425)Online publication date: 6-Jul-2018

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