Minimum Joint Mutual Information Loss-based Optimal Feature Selection Algorithm
基于最小联合互信息亏损的最优特征选择算法

In this paper, a minimum joint mutual information loss-based optimal feature selection algorithm was proposed,which firstly finds a non-discriminate feature subset of the original set via a dynamic incremental searching stratagy,and then eliminates false positives by keeping minimum joint mutual information loss with class in each iteration using a minimal conditional mutual information criterion, in such a way as to obtain an approximate optimal feature subset. Furthermore, for the computationally intractable problem arising in high dimensional feature space that characterizes the existing method of conditional independence test with conditional mutual information, a fast implementation of conditional mutual information estimation was introduced and used to implement the proposed algorithm. Experimental resups for the classification task show that the proposed algorithm performs better than the representative feature selection algorithms. Experimental results for the execution task show that the proposed implementation of conditional mutual information estimation has a considerable advantage.