Published:
May 1999
Proceedings:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Volume
Issue:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Track:
All Papers
Downloads:
Abstract:
Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. There are two common approaches: a wrapper uses the intended learning algorithm itself to evaluate the usefulness of features, while a filter evaluates features according to heuristics based on general characteristics of the data. The wrapper approach is generally considered to produce better feature subsets but runs much more slowly than a filter. This paper describes a new filter approach to feature selection that uses a correlation based heuristic to evaluate the worth of feature subsets When applied as a data preprocessing step for two common machine learning algorithms, the new method compares favourably with the wrapper but requires much less computation.
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FLAIRS
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
ISBN 978-1-57735-080-4
Published by The AAAI Press, Menlo Park, California.