Unsupervised hybrid PSO — Relative reduct approach for feature reduction

Abstract

Feature reduction selects more informative features and reduces the dimensionality of a database by removing the irrelevant features. Selecting features in unsupervised learning scenarios is a harder problem than supervised feature selection due to the absence of class labels that would guide the search for relevant features. Rough set is proved to be efficient tool for feature reduction and needs no additional information. PSO (Particle Swarm Optimization) is an evolutionary computation technique which finds global optimum solution in many applications. This work combines the benefits of both PSO and rough sets for better data reduction. This paper describes a novel Unsupervised PSO based Relative Reduct (US-PSO-RR) for feature selection which employs a population of particles existing within a multi-dimensional space and dependency measure. The performance of the proposed algorithm is compared with the existing unsupervised feature selection methods USQR (UnSupervised Quick Reduct) and USSR (UnSupervised Relative Reduct) and the effectiveness of the proposed approach is measured by using Clustering evaluation indices.