MLTL: A multi-label approach for the Tomek Link undersampling algorithm

Abstract

A large variety of problems are multi-labeled, which made the Multi-Label Classification field become an active topic in the machine learning community. However, real world problems tend to be imbalanced, meaning that some classes may have more samples than others. Learning from imbalanced datasets is a challenging task and for that has attracted the attention of researchers that have proposed some resampling algorithms to address this problem. This work presents two main contributions: A new resampling algorithm for multi-label classification problems named MLTL - Multi-Label Tomek Link, which is based on the standard Tomek Link resampling algorithm; A multi-label imbalanceness API for the Mulan framework. Results in seven well-known datasets showed that MLTL is a competitive technique when compared to other multi-label resampling methods from the literature.