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Computer Science > Machine Learning

arXiv:2305.17152 (cs)
[Submitted on 26 May 2023 (v1), last revised 30 May 2023 (this version, v2)]

Title:mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms

Authors:Antonio J. Rivera, Miguel A. Dávila, David Elizondo, María J. del Jesus, Francisco Charte
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Abstract: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 this http URL, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2305.17152 [cs.LG]
  (or arXiv:2305.17152v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2305.17152

Submission history

From: Francisco Charte [view email]
[v1] Fri, 26 May 2023 10:29:53 UTC (1,608 KB)
[v2] Tue, 30 May 2023 06:23:26 UTC (1,608 KB)
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