Artificial Intelligence in Theory and Practice
Volume 217 of the series IFIP International Federation for Information Processing pp 209216
Two new feature selection algorithms with Rough Sets Theory
 Yailé CaballeroAffiliated withDepartment of Computer Science, University of Camagüey
 , Rafael BelloAffiliated withDepartment of Computer Science, Universidad Central de Las Villas
 , Delia AlvarezAffiliated withDepartment of Computer Science, University of Camagüey
 , Maria M. GarciaAffiliated withDepartment of Computer Science, Universidad Central de Las Villas
Abstract
Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensive computing process although very important in data analysis and knowledge discovery. Because of this, it has been necessary the development of different variants to calculate reducts. The present work look into the utility that offers Rough Sets Model and Information Theory in feature selection and a new method is presented with the purpose of calculate a good reduct. This new method consists of a greedy algorithm that uses heuristics to work out a good reduct in acceptable times. In this paper we propose other method to find good reducts, this method combines elements of Genetic Algorithm with Estimation of Distribution Algorithms. The new methods are compared with others which are implemented inside Pattern Recognition and Ant Colony Optimization Algorithms and the results of the statistical tests are shown.
 Title
 Two new feature selection algorithms with Rough Sets Theory
 Book Title
 Artificial Intelligence in Theory and Practice
 Book Subtitle
 IFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream, August 21–24, 2006, Santiago, Chile
 Pages
 pp 209216
 Copyright
 2006
 DOI
 10.1007/9780387347479_22
 Print ISBN
 9780387346540
 Online ISBN
 9780387347479
 Series Title
 IFIP International Federation for Information Processing
 Series Volume
 217
 Series ISSN
 15715736
 Publisher
 Springer US
 Copyright Holder
 International Federation for Information Processing
 Additional Links
 Topics
 Industry Sectors
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 Editors

 Max Bramer ^{(1)}
 Editor Affiliations

 1. University of Portsmouth
 Authors

 Yailé Caballero ^{(2)}
 Rafael Bello ^{(3)}
 Delia Alvarez ^{(2)}
 Maria M. Garcia ^{(3)}
 Author Affiliations

 2. Department of Computer Science, University of Camagüey, Cuba
 3. Department of Computer Science, Universidad Central de Las Villas, Cuba
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