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Antonio Javier Gallego

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Open access Jul 2026

Exploring the impact of label-level noise on multi-label k-Nearest Neighbor classification

A comprehensive empirical study of the impact of label-level noise on multi-label kNN classification and on Multi-label Prototype Generation (MPG) methods reveals that Additive and Partial Uniform noise are the most detrimental, whereas Subtractive and cardinality-preserving policies are comparatively less harmful.

Antonio Requena, Alejandro Galán-Cuenca, Antonio Javier Gallego et al. · 0 citations

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