Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods

Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many ensemble-based algorithms have been developed to classify multi-label data in an effective manner. There are several factors that differentiate between the various ensembles methods to output a label set prediction for unseen instances: the method of combining the predictions of the base classifiers and the thresholding strategy to implement a decision function. In this paper, we present an extensive empirical study comparing several multi-label ensemble methods over ten benchmark data sets. We also examine the influence of two types of voting schemas and the effect of calibrating the final decision function via single and Multi thresholding strategies on each performance metric. The experimental results were analyzed using statistical test to assess the statistical differences in the predictions performance.