Fast and accurate feature selection using hybrid genetic strategies

Author

Guerra-Salcedo, César ; Chen, Stephen ; Whitley, Darrell ; Smith, Stephen

Author_Institution

Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA

Volume

1

fYear

1999

fDate

1999

Abstract

When dealing with object classification, each object is defined by a set of features (characteristics) that classify the object to a particular class. The problem is how to choose the best subset of characteristics that provide an accurate classification. Previous research has shown that decision tables are as accurate as C4.5 for classification purposes. Two different genetic search techniques, CHC and CF/RSC, are applied to this problem. Results shows that CF/RSC and decision tables are a very good combination when dealing with large feature spaces. Results also suggest that CHC is better when used for problems with noise added to the features

Keywords

feature extraction; genetic algorithms; object recognition; pattern classification; search problems; C4.5; CF/RSC; CHC; decision tables; feature selection; genetic search techniques; hybrid genetic strategies; large feature spaces; object classification; Area measurement; Biological cells; Clouds; Computer science; Genetic algorithms; Image classification; Machine learning algorithms; Robots; Search problems; Space exploration;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.781923

Filename

781923