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Titolo:
Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms
Autore:
Inza, I; Larranaga, P; Sierra, B;
Indirizzi:
Univ Basque Country, Dept Comp Sci & Artificial Intelligence, E-20080 Donostia San Sebastian, Basque Country, Spain Univ Basque Country Donostia SanSebastian Basque Country Spain E-20080 n
Titolo Testata:
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
fascicolo: 2, volume: 27, anno: 2001,
pagine: 143 - 164
SICI:
0888-613X(200108)27:2<143:FSSBBN>2.0.ZU;2-I
Fonte:
ISI
Lingua:
ENG
Soggetto:
CAUSAL NETWORKS; PERFORMANCE;
Keywords:
feature subset selection; estimation of distribution algorithm; soft computing; estimation of Bayesian network algorithm; Bayesian network; predictive accuracy;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
60
Recensione:
Indirizzi per estratti:
Indirizzo: Inza, I Univ Basque Country, Dept Comp Sci & Artificial Intelligence, POB 649, E-20080 Donostia San Sebastian, Basque Country, Spain Univ Basque Country POB 649 Donostia San Sebastian Basque Country Spain E-20080
Citazione:
I. Inza et al., "Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms", INT J APPRO, 27(2), 2001, pp. 143-164

Abstract

In this paper we perform a comparison among FSS-EBNA, a randomized, population-based 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 estimation of Bayesian network algorithm (EBNA) search engine, an algorithm within the estimation of distribution algorithm (EDA) approach. The EDA paradigm is born from the roots of the genetic algorithm (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 paradigmavoids 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 cheap local search mechanism. FSS-EBNA can be seen as a hybrid Soft Computing system, a synergistic combination of probabilistic and evolutionary computing to solve the FSS task. Promising results on a set of real Data Mining domains are achieved by FSS-EBNA in the comparison respect to well-known genetic and sequential search algorithms. (C) 2001 Elsevier Science Inc. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/03/20 alle ore 14:42:54