Multi-objective Genetic Algorithm setup for feature subset selection in clustering
Himanshu Kashyap, Sohini Das, Jayee Bhattacharjee, Ritu Halder, Saptarsi Goswami · 2016
Feature selection is a major preprocessing step in areas related to data mining, pattern recognition or machine learning. Finding a most favorable subset of features, among available combinations is a NP-Complete problem. Despite lots of research in this domain, feature selection for clustering is far from solved. The paper is first of its kind in applying multi objective Genetic Algorithm (GA), for feature selection in clustering for a filter method. The optimization objectives are: (i) Maximizing the Laplacian Score and (ii) Minimizing the inter-attribute correlation. Empirical study has been conducted over 21 datasets and results look promising in terms of amount of feature set reduction achieved. In terms of cluster validity also in more than half of the datasets, the proposed method achieves equivalent or better result.