Multilabel prototype generation for data reduction in k-nearest neighbour classification

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• dc.contributor.author Valero-Mas, Jose J.
• dc.contributor.author Gallego, Antonio Javier
• dc.contributor.author Alonso-Jiménez, Pablo
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2023-03-14T07:11:35Z
• dc.date.available 2023-03-14T07:11:35Z
• dc.date.issued 2023
• dc.description.abstract Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.
• dc.description.sponsorship This research was partially funded by the Spanish Ministerio de Ciencia e Innovación through the MultiScore (PID2020-118447RA-I00) and DOREMI (TED2021-132103A-I00) projects. The first author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”.
• dc.format.mimetype application/pdf
• dc.identifier.citation Valero-Mas JJ, Gallego AJ, Alonso-Jiménez P, Serra X. Multilabel prototype generation for data reduction in k-nearest neighbour classification. Pattern Recognition. 2023 Mar;135:109190. DOI: 10.1016/j.patcog.2022.109190
• dc.identifier.doi http://dx.doi.org/10.1016/j.patcog.2022.109190
• dc.identifier.issn 0031-3203
• dc.identifier.uri http://hdl.handle.net/10230/56207
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Pattern Recognition. 2023 Mar;135:109190
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-118447RA-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TED2021-132103A-I00
• dc.rights © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Multilabel classification
• dc.subject.keyword Prototype generation
• dc.subject.keyword Efficient
• dc.subject.keyword kNN
• dc.title Multilabel prototype generation for data reduction in k-nearest neighbour classification
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion