Conference Proceedings

Effective global approaches for mutual information based feature selection

XV Nguyen, J Chan, S Romano, J Bailey

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14 | Association for Computing Machinery (ACM) | Published : 2014

Abstract

Most current mutual information (MI) based feature selection techniques are greedy in nature thus are prone to sub-optimal decisions. Potential performance improvements could be gained by systematically posing MI-based feature selection as a global optimization problem. A rare attempt at providing a global solution for the MI-based feature selection is the recently proposed Quadratic Programming Feature Selection (QPFS) approach. We point out that the QPFS formulation faces several non-trivial issues, in particular, how to properly treat feature 'self-redundancy' while ensuring the convexity of the objective function. In this paper, we take a systematic approach to the problem of global MI-b..

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