MILES++: A Generalizable Clustering-Based Ensemble Framework for Multiclass Imbalanced Learning

Imbalanced multiclass learning remains challenging due to skewed class distributions, class overlap, and heterogeneous within-class structure. We revisit the Multiclass Imbalance Learning in Ensembles through Selective Sampling (MILES) framework and study two clustering-based variants: MILES \({}^{k}\) , which uses \(k\) -means with an SSE-based heuristic for selecting the number of clusters, and MILES \({}^{FF}\) , which uses FarthestFirst to explore an alternative centroid-based partitioning strategy. Both variants combine clustering-guided selective sampling with resampling to construct diverse and more balanced training subsets, improving representation of difficult classes while preserving local decision structure. We evaluate MILES on eight real-world multiclass datasets spanning different imbalance regimes, overlap levels, and feature complexities. Across these experiments, MILES is consistently competitive in Multiclass Area Under the Curve ( \(MAUC\) ) and achieves its strongest gains in Macro-F1, with statistically significant improvements over several strong ensemble baselines. MILES also achieves strong Micro-F1 on multiple datasets, while its performance on Geometric Mean ( \(G\) -Mean) is competitive but more dataset-dependent. Class-wise analysis further shows that the improvement arises from better recovery of hard classes and systematic reduction of dominant baseline confusion patterns, especially on Page and Satellite, while Hyperspectral highlights a limitation case where high dimensionality and stronger overlap reduce the benefits of centroid-based selective sampling. A glaciology case study on glacier algae prediction further demonstrates the practical utility of the framework. Overall, the results position MILES as a robust and generalizable alternative to conventional boosting, bagging, and cost-sensitive ensembles for multiclass imbalanced learning.