Unsupervised Fuzzy Multivariate Symmetric Uncertainty Feature Selection Based on Constructing Virtual Cluster Representative

Abstract

The data readability, complexity reduction of learning algorithms, and enhancing predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. Among the various methods of feature selection, algorithms based on information theory are effective in selecting useful attributes and eliminating useless ones. Mutual information and symmetric uncertainty based methods can effectively estimate feature relevancy, but the use of bivariate measures ignores possible dependencies among more than two features. To address this limitation, this research introduces a novel unsupervised feature selection method, called Fuzzy Multivariate Symmetric Uncertainty-Feature Selection (FMSU-FS). The proposed method also overcomes the need for discretization for continuous features that causes information loss. To evaluate the effectiveness of the proposed method, FMSU is compared with conventional methods based on information theory. The experimental results on benchmark datasets show improvement of our approach in measuring clustering performance.