Mutual information criterion for feature selection from incomplete data
Feature selection is an important preprocessing step in machine learning and data mining, and feature criterion arises a key issue in the construction of feature selection algorithms. Mutual information is one of the widely used criteria in feature selection, which determines the relevance between features and target classes. Some mutual information-based feature selection algorithms have been extensively studied, but less effort has been made to investigate the feature selection issue in incomplete data. In this paper, combined with the tolerance information granules in rough sets, the mutual information criterion is provided for evaluating candidate features in incomplete data, which not only utilizes the largest mutual information with the target class but also takes into consideration the redundancy between selected features. We first validate the feasibility of the mutual information. Then an effective mutual information-based feature selection algorithm with forward greedy strategy is developed in incomplete data. To further accelerate the feature selection process, the selection of candidate features is implemented in a dwindling object set. Compared with existing feature selection algorithms, the experimental results on different real data sets show that the proposed algorithm is more effective for feature selection in incomplete data at most cases.
mutual information feature selection incomplete data rough sets uncertainty measure