Journal
KNOWLEDGE-BASED SYSTEMS
Volume 89, Issue -, Pages 385-397Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2015.07.019
Keywords
Multilabel classification; Imbalanced learning; Oversampling; Synthetic instance generation
Categories
Funding
- Spanish Ministry of Education under the FPU National Program [AP2010-0068]
- Spanish Ministry of Science and Technology [TIN2011-28488, TIN2012-33856]
- Andalusian regional Projects [P10-TIC-06858, P11-TIC-7765]
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Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this paper the process of synthetic instance generation for multilabel datasets (MLDs) is studied and MLSMOTE (Multilabel Synthetic Minority Over-sampling Technique), a new algorithm aimed to produce synthetic instances for imbalanced MLDs, is proposed. An extensive review on how imbalance in the multilabel context has been tackled in the past is provided, along with a thorough experimental study aimed to verify the benefits of the proposed algorithm. Several multilabel classification algorithms and other multilabel oversampling methods are considered, as well as ensemble-based algorithms for imbalanced multilabel classification. The empirical analysis shows that MLSMOTE is able to improve the classification results produced by existent proposals. (C) 2015 Elsevier B.V. All rights reserved.
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