Typical testors generation based on an evolutionary algorithm Conference Paper

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Overview

abstract

• 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). © 2011 Springer-Verlag.

authors

• Aguirre Salado Carlos Arturo
• Cárdenas Tristán Abraham
• Diaz-Sanchez G.
• Mora-Gonzalez M.
• Piza-Davila I.
• Reyes Cárdenas Oscar
• Sánchez Díaz Guillermo

publication date

• 2011-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• feature selection; Hill climbing; optimization; typical testors Evolutionary method; Exponential complexity; Feature relevance; Hill climbing; Hill climbing algorithms; Mutation process; Supervised classification; typical testors; Univariate marginal distribution algorithms; Evolutionary method; Exponential complexity; Feature relevance determinations; Hill climbing; Hill climbing algorithms; Supervised classification; Typical testors; Univariate marginal distribution algorithms; Feature extraction; Optimization; Feature extraction; Genetic algorithms; Optimization; Genetic algorithms; Evolutionary algorithms

Identity

Digital Object Identifier (DOI)

• 10.1007/978-3-642-23878-9_8

Additional Document Info

start page

• 58

end page

• 65

volume

• 6936 LNCS