Unsupervised hybrid PSO - Quick reduct approach for feature reduction

Abstract

Feature reduction reduces the dimensionality of a database and selects more informative features 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. PSO is an evolutionary computation technique which finds global optimum solution in many applications. Rough set is a powerful tool for data reduction based on dependency between attributes. This work combines the benefits of both PSO and rough sets. This paper describes a novel Unsupervised PSO based Quick Reduct (US-PSO-QR) for feature selection which employs a population of particles existing within a multi-dimensional space. The performance of the proposed algorithm is compared with the existing unsupervised feature selection methods and the efficiency is measured by using K-Means Clustering and Rough K-Means Clustering.

Unsupervised feature selection using binary bat algorithm

“…Selecting features in unsupervised learning is harder than supervised learning. Hannah Inbarani et.al [12] proposed a method for unsupervised feature selection using PSO and rough set theory (RST). Pawlak [13] proposed the RST which is an extension of set theory.…”