Feature Selection as State-Space Search: An Empirical Study in Clustering Problems

Proceedings of the International Symposium on Combinatorial Search, 8

Volume

Vol. 8 No. 1 (2015): Eighth Annual Symposium on Combinatorial Search

Short Papers

In this paper we treat the problem of feature selection in unsupervised learning as a state-space search problem. We introduce three different heuristic functions and perform extensive experiments on datasets with tens, hundreds, and thousands of features. Namely, we test different search algorithms using the heuristic functions we introduce. Our results show that the heuristic search approach for feature selection in unsupervised learning problems can be far superior than traditional baselines such as PCA and random projections.

10.1609/socs.v6i1.18375