Feature ranking for multi-target regression (Q782468)

This paper considers multi-task regression (MTR) where the goal is to learn a model that predicts several target variables simultaneously. In particular the authors address the task of feature ranking to score the importance of descriptive attributes. While there is several work on feature ranking in single-task regression, this paper presents one of the first feature ranking methods for MTR. It introduces two methods for feature ranking: one based on an ensemble of predictive clustering trees and one as an extension of RReliefF. Extensive experimental results are reported to justify the effectiveness of the proposed methods.