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Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection
Abstract
Selecting an optimum subset of features from a large set of features is an important pre- processing step for pattern classification, data mining, or machine learning applications. Feature subset selection basically comprises of defining a criterion function for evaluation of the feature subset and developing a search strategy to find the best feature subset from a large number of feature subsets. Lots of mathematical and statistical techniques have been proposed so far. Recently biologically inspired computing is gaining popularity for solving real world problems for their more flexibility compared to traditional statistical or mathematical techniques. In this chapter, the role of Particle Swarm Optimization (PSO), one of the recently developed bio-inspired evolutionary computational (EC) approaches in designing algorithms for producing optimal feature subset from a large feature set, is examined. A state of the art review on Particle Swarm Optimization algorithms and its hybrids with other soft computing techniques for feature subset selection are presented followed by author’s proposals of PSO based algorithms. Simple simulation experiments with benchmark data sets and their results are shown to evaluate their respective effectiveness and comparative performance in selecting best feature subset from a set of features.
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