Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification. (March 2023)

• Record Type: Journal Article
• Authors: Valero-Mas, Jose J.; Gallego, Antonio Javier; Alonso-Jiménez, Pablo; Serra, Xavier
• Is Part Of: Pattern recognition. Volume 135(2023)
• Publication Date: 2023-03
• Subjects: Multilabel classification -- Prototype generation -- Efficient kNN; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109190
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Multilabel Prototype Generation for efficient k-Nearest Neighbour classification. Four multiclass Prototype Generation methods are adapted to the multilabel space. Evaluation with twelve corpora, three noise scenarios, and different classifiers. Proposals significantly improve performance and reduction rates of reference strategies. Novel adaptations proposed also show significant noise reduction capabilities. Abstract: Prototype Generation (PG) methods are typically considered for improving the efficiency of the k -Nearest Neighbour ( k NN) 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 k NN-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.