Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms

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 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 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