Abstract
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.
Highlights
Variable selection aims at reducing the dimensionality of data
In order to validate the subsets of variables selected by the different considered methods, the ε−Support Vector Regression (ε−SVR) regressor was used, with a radial basis function, because it has already been developed for single output [34] as well as for multi-output [35] datasets
This paper presents a filter-type variable selection technique for single and multi-output regression datasets, using a distance measure based on information theory
Summary
Variable selection aims at reducing the dimensionality of data. It consists of selecting the most relevant variables (attributes) among the set of original ones [1]. This step is crucial for the design of regression and classification systems. In this framework, the term relevant is related to the impact of the variables on the prediction error of the variable to be regressed (target variable). The relevant criterion can be based on the performance of a specific predictor (wrapper method), or on some general relevance measure of the variables for the prediction (filter method).
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