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Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification

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Pattern Recognition and Image Analysis (IbPRIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14062))

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Abstract

Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.

This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033.

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Notes

  1. 1.

    An implementation of this experimental procedure together with the assessed methods are available in: https://github.com/jose-jvmas/imbalance-MPG_IbPRIA23.

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Author information

Authors and Affiliations

  1. U.I. for Computer Research, University of Alicante, Alicante, Spain

    Carlos Penarrubia, Jose J. Valero-Mas, Antonio Javier Gallego & Jorge Calvo-Zaragoza

  2. Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain

    Jose J. Valero-Mas

Authors
  1. Carlos Penarrubia
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  2. Jose J. Valero-Mas
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  3. Antonio Javier Gallego
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  4. Jorge Calvo-Zaragoza
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Corresponding author

Correspondence to Jose J. Valero-Mas .

Editor information

Editors and Affiliations

  1. University of Alicante, Alicante, Spain

    Antonio Pertusa

  2. University of Alicante, Alicante, Spain

    Antonio Javier Gallego

  3. Universitat Politècnica de València, Valencia, Spain

    Joan Andreu Sánchez

  4. IPO Porto, Coimbra, Portugal

    Inês Domingues

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Penarrubia, C., Valero-Mas, J.J., Gallego, A.J., Calvo-Zaragoza, J. (2023). Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-36616-1_2

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