Neighborhood repartition-based oversampling algorithm for multiclass imbalanced data with label noise

The classification of imbalanced data remains one of the most significant topics in contemporary data analysis. Existing classification algorithms tend to favor majority classes, leading to false predictions and difficulties in addressing class overlap or label noise. These challenges are particularly evident in multiclass settings, where the mutual imbalance relationships among classes become more complex. Despite this, the vast majority of research in this field has concentrated on binary problems, while the more difficult multiclass problems are relatively underexplored. In this paper, we propose a novel data-sampling technique, a Multiclass Neighborhood Repartition-based Oversampling (MC-NRO) algorithm. The innovation of this method lies in it considers local data characteristics of each class to constrain the oversampled neighborhood. MC-NRO calculates the mutual potential of different classes to precisely optimize the subregions for generating new instances. By selecting different repartition neighborhoods to meet the needs of specific domain, it can detect outliers and label noise, expand the decision boundary of minority class, and avoid class overlap through data cleaning. The experimental results demonstrate that MC-NRO outperforms other advanced oversampling strategies, ranking first on average across the three evaluation metrics, and exhibits robustness, especially in datasets with high noise levels. More importantly, MC-NRO is highly versatile and can be flexibly applied to various classifiers, and is particularly suitable for processing naturally complex (i.e., not affected by noise) datasets.