Genetic algorithm for feature selection for parallel classifiers
type: Item
instance of: scholarly article
title: Genetic algorithm for feature selection for parallel classifiers
zbMATH Open document ID: 0779.68072
DOI: 10.1016/0020-0190(93)90021-Z
published in: Information Processing Letters
publication date: 1994-01-19T00:00:00Z
review text: Parallel classification is a pattern recognition paradigm which resembles the process of group decision making. The scheme consists of \(r\) first- level decision makers (operating on different subsets of features) and a second-level ``aggregator'' of their classification decisions. The complication of the structure aims rather at a higher classification accuracy than at facilitating the computation process. It can be formally proven that the voting scheme of independent classifiers outperforms the best one of them. Let X be the set of \(n\) features describing the objects. The features selection task for conventional (one level) classifier consists in choosing the best subset of \(X\) in terms of a certain classification criterion. The main difficulty in feature selection is that the searching surface is usually multimodal, and the criterion can hardly be described analytically. This fact precludes the application of the classical searching tools based on criterion derivatives. The problem of feature selection becomes even more cumbersome in the parallel classification scheme because a set of subsets \(S=\{S_ 1,...,S_ r\}\), \(S_ i\subseteq X\), is to be chosen instead of a single subset. Possessing a variety of assets, genetic algorithms (GA) have demonstrated good performance in different optimization fields. On the contrast to many trivial optimization techniques, genetic algorithms can operate on discontinuous, noisy, multidimensional and multimodal surfaces. The feature selection task fits easily GA frameworks because a subset of \(X\) can be encoded as an \(n\)-size chromosome containing `1' at the \(i\)-th position if the respective feature is in the subset, and `0', otherwise. A ``fit'' criterion \(J(S_ k)\) for evaluating the chromosomes is defined which in this case may be a measure of discrimination power or classification accuracy of the respective subset. We consider a voting two-level scheme with three first-level classifiers obtaining different featu…
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 68W15
zbMATH DE Number: 417419
zbMATH Keywords: Parallel classification
zbMATH Keywords: genetic algorithms
author: Ludmila I. Kuncheva
reviewed by: Ludmila I. Kuncheva
MaRDI profile type: Publication
cites work: Statistical Properties of Error Estimators in Performance Assessment of Recognition Systems
cites work: New approaches for heuristic search: A bilateral linkage with artificial intelligence
cites work: Q4692508
cites work: Introduction to computation and learning in artificial neural networks
cites work: Q3493275
cites work: A note on genetic algorithms for large-scale feature selection
full work available at URL: 0020-0190(93)90021-z
OpenAlex ID: W2068989419
description: scientific article; zbMATH DE number 417419