A fuzzy set based approach for effective feature selection

High dimensionality of input dataset is an important issue in machine learning. Though there are a few effective ways of dimensionality reduction, none of them, other than feature selection, preserve the original features of the input dataset. Feature selection assesses relevance of features along with their potential redundancy for dimensionality reduction. This paper proposes a first of its kind fuzzy graph based technique for measuring feature relevance and redundancy. The proposed technique addresses the uncertainty in interpretation of feature relevance and redundancy by defining a fuzzy set based formulation of feature relevance and redundancy. Superior performance of the proposed algorithm is illustrated by simulation experiments with 20 benchmark datasets. The proposed algorithm has demonstrated an average feature reduction of 86% along with a 10% improvement in average classification accuracy compared to the state-of-the-art algorithms.