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Feature selection and visualization based on interaction dominance

Feature selection is an important preprocessing process of classification algorithm. It is useful in many application domains. This paper introduces a new feature selection method called Interaction Dominance Based Feature Selection (IDFS), which employs the dominance of mutual information as a measure of the relevance of feature and the class, the dominance count of three-way interaction information as a measure of feature redundancy. A forward greedy search strategy is used to select features which have maximum sum of dominance count. The experimental results of six real world datasets demonstrate the performance of the proposed method. The method is compared with four Well-known feature ranking methods; Mutual Information Based Feature Selection (MIFS), Minimum Redundancy Maximum Relevance (mRMR), Joint Mutual Information (JMI), and Double Input Symmetrical Relevance (DISR). The average classification accuracy of three different types of classifiers including C4.5, IB1 and PART is used to evaluate the performance of the IDFS method. The results show that IDFS outperforms the other methods.