• Computer Science > Machine Learning [Submitted on 22 Feb 2026] Title:IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning View PDF HTML (experimental)Abstract:Class imbalance, overlap, and noise degrade data quality, reduce model reliability, and limit generalization. • Although widely studied in binary classification, these issues remain underexplored in multi-class settings, where complex inter-class relationships make minority-majority structures unclear and traditional clustering fails to capture distribution shape. • Approaches that rely only on geometric distances risk removing informative samples and generating low-quality synthetic data, while binarization approaches treat imbalance locally and ignore global inter-class dependencies. • At the algorithmic level, ensembles struggle to integrate weak classifiers, leading to limited robustness. • This paper proposes IMOVNO+ (IMbalance-OVerlap-NOise+ Algorithm-Level Optimization), a two-level framework designed to jointly enhance data quality and algorithmic robustness for binary and multi-class tasks. • At the data level, first, conditional probability is used to quantify the informativeness of each sample.

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  • Computer Science > Machine Learning [Submitted on 22 Feb 2026] Title:IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning View PDF HTML (experimental)Abstract:Class imbalance, overlap, and noise degrade data quality, reduce model reliability, and limit generalization. Although widely studied in binary classification, these issues remain underexplored in multi-class settings, where complex inter-class relationships make minority-majority structures unclear and traditional clustering fails to capture distribution shape. Approaches that rely o

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