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[2602.20199] IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning
Summary
The paper introduces IMOVNO+, a framework designed to enhance data quality and algorithmic robustness in imbalanced multi-class learning by addressing issues of class imbalance, overlap, and noise.
Why It Matters
This research is significant as it tackles the persistent challenges of class imbalance in multi-class settings, which can severely impact model performance. By improving data quality and integrating weak classifiers more effectively, IMOVNO+ could lead to more reliable machine learning applications across various domains.
Key Takeaways
- IMOVNO+ enhances data quality by partitioning datasets into core, overlapping, and noisy regions.
- The framework employs a smart oversampling algorithm to control synthetic sample proximity and reduce overlaps.
- Evaluation on 35 datasets shows IMOVNO+ consistently outperforms state-of-the-art methods in both binary and multi-class tasks.
Computer Science > Machine Learning arXiv:2602.20199 (cs) [Submitted on 22 Feb 2026] Title:IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning Authors:Soufiane Bacha, Laouni Djafri, Sahraoui Dhelim, Huansheng Ning View a PDF of the paper titled IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning, by Soufiane Bacha and 3 other authors 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. Se...
