Feature subset selection by particle swarm optimization with fuzzy fitness function

An algorithm based on particle swarm optimization with fuzzy fitness function has been proposed for getting optimal feature subset from a feature set with large number of features and is computationally less demanding in comparison to genetic algorithm.

Abstract

Feature extraction or feature subset selection is an important preprocessing task for pattern recognition, data mining or machine learning application. Feature subset selection basically depends on selecting a criterion function for evaluation of the feature subset and a search strategy to find the best feature subset from a large number of feature subsets. Lots of techniques have been developed so far, mainly from statistical theory, still research is going on to find better solutions in terms of optimality and computational ease. Recently soft computing techniques are gaining popularity for solving real world problems for their more flexibility compared to statistical or mathematical techniques. In this work an algorithm based on particle swarm optimization with fuzzy fitness function has been proposed for getting optimal feature subset from a feature set with large number of features. Simple simulation experiments with two benchmark data sets show that the proposed method is similar in performance to the results reported earlier and is computationally less demanding in comparison to genetic algorithm, another population based evolutionary search technique proposed earlier for feature subset selection by author.