Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification

TL;DR

This paper introduces four novel multilabel prototype generation strategies for k-NN classifiers, significantly improving efficiency and robustness in noisy data scenarios while maintaining classification performance.

Contribution

It adapts four multiclass prototype generation methods to multilabel data, filling a gap in existing research and offering configurable solutions for efficiency and effectiveness.

Findings

Significant efficiency improvements over existing methods.

Enhanced robustness in noisy data scenarios.

Maintains or improves classification accuracy.

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 a 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.