Mutual Information-Based Feature Selection from Set-Valued Data

Abstract

In many machine learning and data mining applications, it may happen that the data acquired for classification analysis are set-valued, i.e., The feature values of an object set are set-valued, which can be used to characterize uncertain information in decision making tasks. Set-valued data are the generalized models of single-valued data. Some mutual information-based feature selection algorithms have been extensively studied, but less effort has been made to investigate the feature selection issue with the mutual information analysis in set-valued data. Just owing to these, mutual information is firstly introduced in the set-valued data in this paper. Unlike the traditional computations, the mutual information is estimated on the unmarked objects. Correspondingly, a feature selection algorithm based on mutual information is developed, which is implemented in a dwindling universe to quicken the feature selection process. Compared with the state-of-the-art methods, the experimental results on different data sets demonstrate the efficiency and effectiveness of the proposed algorithm in set-valued data.