Benchmarking Multi-label Classification Algorithms

Multi-label classification is an approach to classification prob- lems that allows each data point to be assigned to more than one class at the same time. Real life machine learning problems are often multi-label in nature—for example image labelling, topic identification in texts, and gene expression prediction. Many multi-label classification algorithms have been proposed in the literature and, although there have been some benchmarking experiments, many questions still remain about which ap- proaches perform best for certain kinds of multi-label datasets. This pa- per presents a comprehensive benchmark experiment of eleven multi- label classification algorithms on eleven different datasets. Unlike many existing studies, we perform detailed parameter tuning for each algorithm- dataset pair so as to allow a fair comparative analysis of the algorithms. Also, we report on a preliminary experiment which seeks to understand how the performance of different multi-label classification algorithms changes as the characteristics of multi-label datasets are adjusted.