Extensive experimental comparison among multilabel methods focused on ranking performance

Multilabel classification is a growing paradigm in the fields of data mining and machine learning. In multilabel learning, each input sample can belong to more than one binary class (termed "label") in a multilabel framework, with contrasts with the standard single-label learning scenario. The challenge of producing a better classifier lies in the advantageous use of the correlations among different labels. In recent years, many multilabel models have been proposed, making it difficult to decide which methods to use. In this paper, we present the most comprehensive ranking performance comparison carried out thus far among numerous methods. We conduct a comprehensive analysis by comparing several configurations of 56 distinct methods, resulting in a total of 173 trained models. In addition, we utilize an extensive collection of problems involving 65 datasets. Furthermore, we analyze the effectiveness of the tested techniques by evaluating their performance with six different ranking performance metrics. Our findings indicate that while certain strategies consistently rank highly among the top-performing models, the most effective methods are strongly correlated with the specific metrics used to assess their performance. Finally, we examine many behavioral aspects of the different approaches.