Integration of Soft Computing Approaches for Feature Subset Selection

Abstract

Feature subset selection basically depends on the design of a criterion function to measure the effectiveness of a particular feature or a feature subset and the selection of a search strategy to find out he best feature subset. Lots of techniques, mostly statistical, have been developed so far which are mainly categorized into classifier independent filter approaches and classifier dependant wrapper approaches. Wrapper approaches produce good results but are computationally unattractive specially when nonlinear neural classifiers with complex learning algorithms are used The present work proposes some hybrid algorithms for feature subset selection using individual tools from soft computing paradigm taking advantage of both the filter and wrapper approaches. Artificial neural network, fuzzy logic and genetic algorithm are used to design neuro fuzzy and fuzzy genetic algorithms. A fuzzy set theoretic measure for assessing the goodness of a feature is used in conjunction with a multilayer perceptron (MLP) or a fractal neural network (FNN), the proposed modification of MLP having a statistically fractal sparse architecture. Though the process does not guarantee absolute optimality, the selected feature subset produces near optimal results for practical purposes. The process is less time consuming and computationally light compared to any neural network classifier based sequential feature subset selection technique. The same measure in conjunction with genetic algorithm has been used and it is found that fuzzy genetic algorithm is better than neuro fuzzy algorithms for large feature set problems for finding out a near optimal solution. The proposed algorithms have been simulated with two different data sets to show their effectiveness.