Estimating Redundancy Information of Selected Features in Multi-dimensional Pattern Classification

This paper proposes a novel criterion for estimating the redundancy information of selected feature sets in multi-dimensional pattern classification. An appropriate feature selection process typically maximizes the relevancy of features to each class and minimizes the redundancy of features between selected features. Unlike to the relevancy information that can be measured by mutual information, however, it is difficult to estimate the redundancy information because its dynamic range is varied by the characteristics of features and classes.

By utilizing the conceptual diagram of the relationship between candidate features, selected features, and class variables, this paper proposes a new criterion to accurately compute the amount of redundancy. Specifically, the redundancy term is estimated by conditional mutual information between selected and candidate features to each class variable, which does not need a cumbersome normalization process as the conventional algorithm does. The proposed algorithm is implemented into a speech/music discrimination system to evaluate classification performance. Experimental results by varying the number of selected features verify that the proposed method shows higher classification accuracy than conventional algorithms.