Insights into imbalance-aware Multilabel Prototype Generation mechanisms for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si232.svg" display="inline" id="d1e1324"> <mml:mi>k</mml:mi> </mml:math> -Nearest Neighbor classification in

Prototype Generation (PG) techniques enhance the efficiency of the k -Nearest Neighbor ( k NN) classifier by condensing datasets through the use of specific rules. More precisely, these strategies work on the premise of merging the elements in the reference data collection to generate an alternative and more compact data assortment that substitutes the former one without remarkably affecting the recognition performance. Nevertheless, despite being widely studied in multiclass scenarios, PG is still underexplored in multilabel contexts, leading to limitations, notably in the handling of label imbalance and noise. In this regard, this work introduces a reduction framework that allows for multilabel PG methods to handle these challenges of label imbalance and noise. The proposed mechanisms comprise a selection strategy that exclusively preserves noise-free samples in the process, a mechanism to avoid severely imbalanced samples from being inadequately processed, and two new merging policies for the PG methods to generate novel samples. These enhancements are considered along with three established multilabel PG methods: Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning. Evaluations are conducted using three k NN-based multilabel classifiers and 12 diverse datasets with different levels of label imbalance. We additionally study the performance with varying values of k under different label-noise scenarios. The results are assessed through statistical tests and indicate that our proposals outperform the original methods that disregard label imbalance, even in the presence of noise, thus validating these approaches and fostering further research in the field. • Multilabel data reduction framework for efficient k-Nearest Neighbor classification. • Addressing scenarios with class imbalance and label-level noise. • Data reduction based on Prototype Generation strategies for multilabel learning. • Evaluation on twelve datasets with different rates of label noise and data imbalance. • Proposals significantly improve efficiency while mitigating the effect of the noise.