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  1. International Journal of Machine Learning and Cybernetics
  2. International Journal of Machine Learning and Cybernetics : Volume 4
  3. International Journal of Machine Learning and Cybernetics : Volume 4, Issue 3, June 2013
  4. An enhanced XCS rule discovery module using feature ranking
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International Journal of Machine Learning and Cybernetics : Volume 8
International Journal of Machine Learning and Cybernetics : Volume 7
International Journal of Machine Learning and Cybernetics : Volume 6
International Journal of Machine Learning and Cybernetics : Volume 5
International Journal of Machine Learning and Cybernetics : Volume 4
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 6, December 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 5, October 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 4, August 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 3, June 2013
An enhanced XCS rule discovery module using feature ranking
Design of custom-made stacked patch antennas: a machine learning approach
On the structure of definable sets in covering approximation spaces
Spatial pooling for greyscale images
Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues
Minimizing data consumption with sequential online feature selection
Lattice-valued information systems based on dominance relation
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 2, April 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 1, February 2013
International Journal of Machine Learning and Cybernetics : Volume 3
International Journal of Machine Learning and Cybernetics : Volume 2
International Journal of Machine Learning and Cybernetics : Volume 1

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An enhanced XCS rule discovery module using feature ranking

Content Provider SpringerLink
Author Abedini, Mani Kirley, Michael
Copyright Year 2012
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, affect model performance. Numerical simulations have shown that our guided rule discovery mechanism improves the performance of XCS in terms of accuracy, execution time and more generally in terms of classifier diversity in the population, especially for high-dimensional classification problems. We present a detailed discussion of the effects of model parameters and recommend settings for large scale problems.
Starting Page 173
Ending Page 187
Page Count 15
File Format PDF
ISSN 18688071
Journal International Journal of Machine Learning and Cybernetics
Volume Number 4
Issue Number 3
e-ISSN 1868808X
Language English
Publisher Springer-Verlag
Publisher Date 2012-03-18
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Learning classifier systems Genetics-based machine learning XCS Feature ranking High-dimensional classification Microarray gene expression profiles Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type Text
Resource Type Article
Subject Artificial Intelligence Computer Vision and Pattern Recognition Software
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