Markov Blanket Feature Selection for Support Vector Machines

Jianqiang Shen, Lida Li, Weng-Keen Wong

Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery approach for support vector machines and propose to discover Markov blankets by performing a fast heuristic Bayesian network structure learning. We give a sufficient condition that our approach will improve the performance. Two major factors that make it prohibitive for learning Bayesian networks from high-dimensional data sets are the large search space and the expensive cycle detection operations. We propose to restrict the search space by only considering the promising candidates and detect cycles using an online topological sorting method. Experimental results show that we can efficiently reduce the feature dimensionality while preserving a high degree of classification accuracy.

Subjects: 12. Machine Learning and Discovery; 3.4 Probabilistic Reasoning

Submitted: Apr 12, 2008


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