Typical Testors Generation Based on an Evolutionary Algorithm

Typical testors are useful for both feature selection and feature relevance determination in supervised classification problems. However, reported algorithms that address the problem of finding the set of all typical testors have exponential complexity. In this paper, we propose to adapt an evolutio...

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Bibliographic Details
Published inIntelligent Data Engineering and Automated Learning - IDEA 2011 Vol. 6936; pp. 58 - 65
Main Authors Diaz-Sanchez, German, Piza-Davila, Ivan, Sanchez-Diaz, Guillermo, Mora-Gonzalez, Miguel, Reyes-Cardenas, Oscar, Cardenas-Tristan, Abraham, Aguirre-Salado, Carlos
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2011
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
feature selection
Hill climbing
Information retrieval
optimization
typical testors
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Summary:Typical testors are useful for both feature selection and feature relevance determination in supervised classification problems. However, reported algorithms that address the problem of finding the set of all typical testors have exponential complexity. In this paper, we propose to adapt an evolutionary method, the Hill-Climbing algorithm, with an acceleration operator in mutation process, to address this problem in polinomial time. Experimental results with the method proposed are presented and compared, in efficiency, with other methods, namely, Genetic Algorithms (GA) and Univariate Marginal Distribution Algorithm (UMDA).
ISBN:9783642238772
3642238777
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-23878-9_8