A feature selection framework based on supervised data clustering

Feature selection is an important step for data mining and machine learning to deal with the curse of dimensionality. In this paper, we propose a novel feature selection framework based on supervised data clustering. Instead of assuming there only exists low-order dependencies between features and the target variable, the proposed method directly estimates the high-dimensional mutual information between a candidate feature subset and the target variable through supervised data clustering. In addition, it can automatically determine the number of features to be selected instead of manually setting it in a prior. Experimental results show that the proposed method performs similar or better compared with state-of-the-art feature selection methods.