- (Nguyen et al., 2014) ⇒ Xuan Vinh Nguyen, Jeffrey Chan, Simone Romano, and James Bailey. (2014). “Effective Global Approaches for Mutual Information based Feature Selection.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623611
- Feature evaluation and selection; feature selection; global optimization; mutual information; semi-definite programming; spectral relaxation
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-based feature selection. We show how the resulting NP-hard global optimization problem could be efficiently approximately solved via spectral relaxation and semi-definite programming techniques. We experimentally demonstrate the efficiency and effectiveness of these novel feature selection frameworks.
|2014 EffectiveGlobalApproachesforMut||Xuan Vinh Nguyen|
|Effective Global Approaches for Mutual Information based Feature Selection||10.1145/2623330.2623611||2014|
|Author||Xuan Vinh Nguyen +, Jeffrey Chan +, Simone Romano + and James Bailey +|
|proceedings||Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Effective Global Approaches for Mutual Information based Feature Selection +|