Fuzzy Consistency Measure with Particle Swarm Optimization for Feature Selection

Feature selection or dimensionality reduction is an important task for any pattern recognition, data mining or machine learning problem. For selection of the optimal subset of relevant features, two steps are needed. In the first step a measure is designed for the evaluation of a candidate feature subset and in the second step, search through the feature space is done for selecting the optimal one. Existing feature selection methodologies use combinations of various evaluation measures and search strategies for selecting optimal feature subset. Though a large number of effective methodologies are already developed, none of them is perfect. Research is still going on to find better algorithm with lesser computational cost. In this work a fuzzy consistency based evaluation measure has been proposed. Consequently a feature selection algorithm using the proposed fuzzy consistency measure with particle swarm optimization, an evolutionary computational technique widely used for optimization problems, is developed for selecting optimal feature subset. Simple simulation experiments with bench mark data sets have been done and the simulation results provide evidence that the proposed algorithm might be a good candidate for selecting optimal feature subset.