Ensemble of classifier chains and decision templates for multi-label classification

Multi-label classification is the task of inferring the set of unseen instances using the knowledge obtained through the analysis of a set of training examples with known label sets. In this paper, a multi-label classifier fusion ensemble approach named decision templates for ensemble of classifier chains is presented, which is derived from the decision templates method. The proposed method estimates two decision templates per class, one representing the presence of the class and the other representing its absence, based on the same examples used for training the set of classifiers. For each unseen instance, a new decision profile is created and the similarity between the decision templates and the decision profile determines the resulting label set. The method is incorporated into a traditional multi-label classifier algorithm: the ensemble of classifier chains. Empirical evidence indicates that the use of the proposed decision templates adaptation can improve the performance over the traditionally used combining schemes, especially for domains with a large number of instances available, improving the performance of an already high-performing multi-label learning method.