Heuristic search over a ranking for feature selection

Authors:
Roberto Ruiz;José C. Riquelme;Jesús S. Aguilar-Ruiz
Affiliations:
Department of Computer Science, University of Seville, Spain;Department of Computer Science, University of Seville, Spain;Department of Computer Science, University of Seville, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well–known data sets, coupled with notable dimensionality reduction.