Imbalance in Multilabel Datasets

Abstract

The frequency of class labels in many datasets is not even. On the contrary, that a certain class appears in a large portion of the data samples while other is scarcely represented is something quite usual. This casuistic produces a problem generically labeled as class imbalance. Due to these differences between class distributions, a specific need arises, imbalanced learning. This chapter beings introducing the mentioned task in Sect. 8.1. Then, the specific aspects of imbalance in the multilabel area are discussed in Sect. 8.2. Section 8.3 explains how imbalance in MLC has been faced, enumerating a considerable set of proposals. Some of them are experimentally evaluated in Sect. 8.4. Lastly, Sect. 8.5 summarizes the contents.