Feature Selection Algorithm-based Approximate Markov Blanket and Dynamic Mutual Information
基于近似Markov Blanket和动态互信息的特征选择算法

To resolve the poor performance of classification owing to the irrelevant and redundancy features, feature selection algorithm based on approximate Markov Blanket and dynamic mutual information was proposed. The algorithm uses mutual information as the evaluation criteria of feature relevance, which is dynamically estimated on the unrecognixed samples. Redundancy features were removed exactly by approximate Markov Blanket. So a small size feature subset can be attained with the proposed algorithm. To attest the validity,we made experiments on UCI data sets with support vector machine as the classifier, compared with DMIFS and RcliefF algorithms. Experiments result suggest that,compared with original feature set,the feature subset size obtained by the proposed algorithm is much less than original feature set and performance on actual classification is better than or as good as that by original feature set.