An ensemble-based approach for multi-view multi-label classification

Multi-label classification with multiple data views is a recent research field not much explored. This more flexible learning approach allows each pattern to be represented by several sets of attributes and each pattern can have simultaneously associated several labels. In this work, an ensemble-based approach, which enables the fusion of views at decision level by majority voting, is proposed. The study carried out on four data sets considering 27 multi-label evaluation metrics shows that our proposal overcomes and improves the results obtained by the individual views as well as the execution time and the performance of the classic approach which concatenates all the views in a single set of features.