A Novel Approach for Feature Selection

Abstract

Clustering problem suffers from the curse of dimensionality. Dimensionality reduction of a feature set refers to the problem of selecting relevant features which produce the most predictive outcome and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction techniques, and does not assume any data distribution model. Our method associates to each cluster a weight vector, whose values capture the relevance of features within the corresponding cluster. To judge the efficiency of the proposed method the results are experimentally compared with other optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for feature selection.