ליאור רוקח

A number of ensemble algorithms for solving multi-label classification problems have been proposed in recent years. Diversity among the base learners is known to be important for constructing a good ensemble. In this paper we define a method for introducing diversity among the base learners of one of the previously presented multi-label ensemble classifiers. An empirical comparison on 10 datasets demonstrates that model diversity leads to an improvement in prediction accuracy in 80% of the evaluated cases. Additionally, in most cases the proposed “diverse” ensemble method outperforms other multi-label ensembles as well.