Title | A First Approach to Deal with Imbalance in Multi-label Datasets |
Publication Type | Conference Paper |
Year of Publication | 2013 |
Authors | Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. |
Conference Name | 8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013) |
Pagination | 150-160 |
Date Published | 9 |
Conference Location | Salamanca (Spain) |
ISBN Number | 978-3-642-40845-8 |
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 |
DOI | 10.1007/978-3-642-40846-5_16 |
A First Approach to Deal with Imbalance in Multi-label Datasets
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