Investigating the Impact of Diversity in Ensembles of Multi-label Classifiers

In the last decades, the use of ensembles of classifiers in different domains of application has received significant attention of the Machine Learning community. In a typical architecture of ensemble, a new input pattern is presented to all K components. The individual classifiers provide their output and send them to a combination method, which is responsible for providing the final output. Ensemble of classifiers can be applied to any classification problem, single-label or multi- label problems. Multi-label (ML) classification has been received much attention from various research domains, such as text categorization, bioinformatics, computer vision, among others. In this paper, we investigate the use of ensemble of classifiers when applied to multi-label problems, focusing on the use of diversity measures in these systems when applied to multi-label problems. In order to do this, the present work proposes an adapted approach for two well-known diversity measures, good and bad, for ensembles of multi-label classifiers. In order to assess the feasibility of the proposed diversity measures in ensemble of multi-label classifiers, an empirical analysis is conducted, with eight multi-label classification problems. Generally, our finds indicate that there is a strong correlation between performance of the ML ensembles with the proposed diversity measures, showing that these diversity measures can be used as an important tool in the design of efficient ensembles of multi-label classifiers.