Applications of approximate reducts to the feature selection problem

Authors:
Andrzej Janusz;Sebastian Stawicki
Affiliations:
Faculty of Mathematics, Informatics, and Mechanics, The University of Warsaw, Warszawa, Poland;Faculty of Mathematics, Informatics, and Mechanics, The University of Warsaw, Warszawa, Poland
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

In this paper we overview two feature rankings methods that utilize basic notions from the rough set theory, such as the idea of the decision reducts. We also propose a new algorithm, called Rough Attribute Ranker. In our approach, the usefulness of features is measured by their impact on quality of the reducts that contain them. We experimentally compare the reduct-based methods with several classic attribute rankers using synthetic, as well as real-life high dimensional datasets.