Feature Selection via Pareto Multi-objective Genetic Algorithms

ABSTRACT

Feature selection, an important combinatorial optimization problem in data mining, aims to find a reduced subset of features of high quality in a dataset. Different categories of importance measures can be used to estimate the quality of a feature subset. Since each measure provides a distinct perspective of data and of which are their important features, in this article we investigate the simultaneous optimization of importance measures from different categories using multi-objective genetic algorithms grounded in the Pareto theory. An extensive experimental evaluation of the proposed method is presented, including an analysis of the performance of predictive models built using the selected subsets of features. The results show the competitiveness of the method in comparison with six feature selection algorithms. As an additional contribution, we conducted a pioneer, rigorous, and replicable systematic review on related work. As a result, a summary of 93 related papers strengthens features of our method.

Acknowledgments

We would also like to thank Aurora T. R. Pozo and Antonio R. S. Parmezan for their collaboration.

Additional information

Funding

The authors would like to thank the Brazilian National Council for Scientific and Technological Development (CNPq) (grants 482222/2013-1 and 308232/2011-9), the São Paulo Research Foundation (FAPESP) (grants 2012/22608-8 and 2009/12963-2), the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Federal University of ABC for the financial support provided.