Filter-Type Variable Selection Based on Information Measures for Regression Tasks

This paper presents a supervised variable selection method applied to regression problems. This method selects the variables applying a hierarchical clustering strategy based on information measures. The proposed technique can be applied to single-output regression datasets, and it is extendable to multi-output datasets. For single-output datasets, the method is compared against three other variable selection methods for regression on four datasets. In the multi-output case, it is compared against other state-of-the-art method and tested using two regression datasets. Two different figures of merit are used (for the single and multi-output cases) in order to analyze and compare the performance of the proposed method.