Valero-Mas JJ, Gallego AJ, Alonso-Jiménez P, Serra X

SERRA, XAVIER

Scholarly articles

Pattern Recognition

2023

135

190190-

0031-3203

Published

Prototype Generation (PG) methods are typically considered for improving the efficiency of the -Nearest Neighbour (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 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.

Valero-Mas JJ, Gallego AJ, Alonso-Jiménez P, Serra X. Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification. Pattern Recognition 2023; 135( ).