Evidential Multi-label Classification Using the Random k-Label Sets Approach

Multi-label classification deals with problems in which each instance can be associated with a set of labels. An effective multi-label method, named RAkEL, randomly breaks the initial set of labels into smaller sets and trains a single-label classifier in each of this subset. To classify an unseen instance, the predictions of all classifiers are combined using a voting process. In this paper, we adapt the RAkEL approach under the belief function framework applied to set-valued variables. Using evidence theory makes us able to handle lack of information by associating a mass function to each classifier and combining them conjunctively. Experiments on real datasets demonstrate that our approach improves classification performances.