Genetic algorithm for feature selection for parallel classifiers

doi:10.1016/0020-0190(93)90021-Z

Information Processing Letters

Volume 46, Issue 4, 25 June 1993, Pages 163-168

https://doi.org/10.1016/0020-0190(93)90021-Z Get rights and content

Abstract

A way to select a combination of feature subsets serving as inputs for a parallel classifier is described. A genetic algorithm with a properly modified fitness function is used. Experimental results with three sets of real data from internal, neonatal and aviation medicine are reported.

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Cited by (25)

Diversity in search strategies for ensemble feature selection
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Citation Excerpt :
Genetic algorithms have been shown to be effective global optimization techniques in feature subset selection. The use of genetic algorithms for ensemble feature selection was first proposed by Kuncheva [18] and further elaborated in [19]. As the fitness function in [18,19] the accuracy of a combination of classifiers is first proposed and not that of the individual classifiers.
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subsets. A number of ways are known to quantify diversity in ensembles of classifiers, and little research has been done about their appropriateness to ensemble feature selection. In this paper, we compare five measures of diversity with regard to their possible use in ensemble feature selection. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the ensemble accuracy and other characteristics for the ensembles built with ensemble feature selection based on the considered measures of diversity. We consider four search strategies for ensemble feature selection together with the simple random subspacing: genetic search, hill-climbing, and ensemble forward and backward sequential selection. In the experiments, we show that, in some cases, the ensemble feature selection process can be sensitive to the choice of the diversity measure, and that the question of the superiority of a particular measure depends on the context of the use of diversity and on the data being processed. In many cases and on average, the plain disagreement measure is the best. Genetic search, kappa, and dynamic voting with selection form the best combination of a search strategy, diversity measure and integration method.
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A new method for Feature Subset Selection in machine learning, FSS-EBNA (Feature Subset Selection by Estimation of Bayesian Network Algorithm), is presented. FSS-EBNA is an evolutionary, population-based, randomized search algorithm, and it can be executed when domain knowledge is not available. A wrapper approach, over Naive-Bayes and ID3 learning algorithms, is used to evaluate the goodness of each visited solution. FSS-EBNA, based on the EDA (Estimation of Distribution Algorithm) paradigm, avoids the use of crossover and mutation operators to evolve the populations, in contrast to Genetic Algorithms. In absence of these operators, the evolution is guaranteed by the factorization of the probability distribution of the best solutions found in a generation of the search. This factorization is carried out by means of Bayesian networks. Promising results are achieved in a variety of tasks where domain knowledge is not available. The paper explains the main ideas of Feature Subset Selection, Estimation of Distribution Algorithm and Bayesian networks, presenting related work about each concept. A study about the `overfitting' problem in the Feature Subset Selection process is carried out, obtaining a basis to define the stopping criteria of the new algorithm.
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The term evolutionary computation encompasses a host of methodologies inspired by natural evolution that are used to solve hard problems. This paper provides an overview of evolutionary computation as applied to problems in the medical domains. We begin by outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems. We then describe how evolutionary algorithms are applied to solve medical problems, including diagnosis, prognosis, imaging, signal processing, planning, and scheduling. Finally, we provide an extensive bibliography, classified both according to the medical task addressed and according to the evolutionary technique used.
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Genetic algorithm for feature selection for parallel classifiers

Abstract

Comput. Biology Medicine

European J. Oper. Res.

Pattern Recognition Lett.

Comput. Biology Medicine

European J. Oper. Res.

Internat. J. Man-Machine Studies

Adaptive system design: A genetic approach

IEEE Trans. Systems Man Cybernet.