A First Approach to Deal with Imbalance in Multi-label Datasets

TitleA First Approach to Deal with Imbalance in Multi-label Datasets
Publication TypeConference Paper
Year of Publication2013
AuthorsCharte, Francisco, Rivera Antonio J., del Jesus M. J., and Herrera F.
Conference Name8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013)
Pagination150-160
Date Published9
Conference LocationSalamanca (Spain)
ISBN Number978-3-642-40845-8
Abstract

The process of learning from imbalanced datasets has been deeply studied for binary and multi-class classification. This problem also affects to multi-label datasets. Actually, the imbalance level in multi-label datasets uses to be much larger than in binary or multi-class datasets. Notwithstanding, the proposals on how to measure and deal with imbalanced datasets in multi-label classification are scarce. In this paper, we introduce two measures aimed to obtain information about the imbalance level in multi-label datasets. Furthermore, two preprocessing methods designed to reduce the imbalance level in multi-label datasets are proposed, and their effectiveness is validated experimentally. Finally, an analysis for determining when these methods have to be applied depending on the dataset characteristics is provided.

Notes

TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858

DOI10.1007/978-3-642-40846-5_16