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Feature Subset Selection by Bayesian networks: a comparison with genetic and sequential algorithms
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| Citations: | 53 - 13 self |
BibTeX
@MISC{Inza_featuresubset,
author = {I. Inza and P. Larrañaga and B. Sierra},
title = {Feature Subset Selection by Bayesian networks: a comparison with genetic and sequential algorithms},
year = {}
}
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Abstract
In this paper we perform a comparison among FSS-EBNA, a randomized, populationbased and evolutionary algorithm, and two genetic and other two sequential search approaches in the well known Feature Subset Selection (FSS) problem. In FSS-EBNA, the FSS problem, stated as a search problem, uses the EBNA (Estimation of Bayesian Network Algorithm) search engine, an algorithm within the EDA (Estimation of Distribution Algorithm) approach. The EDA paradigm is born from the roots of the GA community in order to explicitly discover the relationships among the features of the problem and not disrupt them by genetic recombination operators. The EDA paradigm avoids the use of recombination operators and it guarantees the evolution of the population of solutions and the discovery of these relationships by the factorization of the probability distribution of best individuals in each generation of the search. In EBNA, this factorization is carried out by a Bayesian network induced by a chea...
Keyphrases
bayesian network feature subset selection sequential algorithm eda paradigm f problem distribution algorithm recombination operator search engine search problem probability distribution ga community sequential search approach genetic recombination operator evolutionary algorithm bayesian network algorithm
