Classifier Selection Approaches for Multi-label Problems

Abstract

While it is known that multiple classifier systems can be effective also in multi-label problems, only the classifier fusion approach has been considered so far. In this paper we focus on the classifier selection approach instead. We propose an implementation of this approach specific to multi-label classifiers, based on selecting the outputs of a possibly different subset of multi-label classifiers for each class. We then derive static selection criteria for the macro- and micro-averaged F measure, which is widely used in multi-label problems. Preliminary experimental results show that the considered selection strategy can exploit the complementarity of an ensemble of multi-label classifiers more effectively than selection approaches analogous to the ones used in single-label problems, which select the outputs of the same classifier subset for all classes. Our results also show that the derived selection criteria can provide a better trade-off between the macro- and micro-averaged F measure, despite it is known that an increase in either of them is usually attained at the expense of the other one.