mldr.resampling: Efficient reference implementations of multilabel resampling algorithms

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes li...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2023-11, Vol.559, p.126806, Article 126806
Main Authors: Rivera, Antonio J., Dávila, Miguel A., Elizondo, D., del Jesus, María J., Charte, Francisco
Format: Article
Language:English
Subjects:
Imbalanced learning
Multilabel learning
R software package
Resampling algorithms
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Summary:Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper. This Original Software Publication presents mldr.resampling, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2023.126806