Document
A First Approach to Deal with Imbalance in Multi-label Datasets
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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.
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Year of Publication |
2013
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Date Published |
9
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Conference Location |
Salamanca (Spain)
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ISBN Number |
978-3-642-40845-8
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DOI |
10.1007/978-3-642-40846-5_16
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Download citation | |
Number of Pages |
150-160
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Bibliography media |
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Notes |
TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858 |