Genetic Algorithm with Fuzzy Operators for Feature Subset Selection(<Special Section>Nonlinear Theory and Its Applications)

概要

論文の詳細を見る
Feature subset selection is an important pre-processing task for pattern recognition, machine learning or datamining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.
一般社団法人電子情報通信学会の論文
2002-09-01