IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning
arXiv:2602.20199v1 Announce Type: new 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 probabil
arXiv:2602.20199v1 Announce Type: new 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. Second, the dataset is partitioned into core, overlapping, and noisy regions. Third, an overlapping-cleaning algorithm is introduced that combines Z-score metrics with a big-jump gap distance. Fourth, a smart oversampling algorithm based on multi-regularization controls synthetic sample proximity, preventing new overlaps. At the algorithmic level, a meta-heuristic prunes ensemble classifiers to reduce weak-learner influence. IMOVNO+ was evaluated on 35 datasets (13 multi-class, 22 binary). Results show consistent superiority over state-of-the-art methods, approaching 100% in several cases. For multi-class data, IMOVNO+ achieves gains of 37-57% in G-mean, 25-44% in F1-score, 25-39% in precision, and 26-43% in recall. In binary tasks, it attains near-perfect performance with improvements of 14-39%. The framework handles data scarcity and imbalance from collection and privacy limits.
Executive Summary
This article proposes IMOVNO+, a two-level framework designed to enhance data quality and algorithmic robustness for imbalanced multi-class learning tasks. The framework employs conditional probability to quantify sample informativeness, partitions the dataset into core, overlapping, and noisy regions, and cleans overlaps using Z-score metrics and big-jump gap distance. Additionally, it uses multi-regularization controls to prevent synthetic sample overlaps and prunes ensemble classifiers to reduce weak-learner influence. Evaluations on 35 datasets show consistent superiority over state-of-the-art methods, with significant gains in G-mean, F1-score, precision, and recall. IMOVNO+ effectively handles data scarcity and imbalance from collection and privacy limits, offering a promising solution for real-world applications.
Key Points
- ▸ IMOVNO+ is a two-level framework for enhancing data quality and algorithmic robustness for imbalanced multi-class learning tasks.
- ▸ The framework employs conditional probability, overlapping-cleaning, and smart oversampling algorithms to improve data quality.
- ▸ IMOVNO+ uses a meta-heuristic ensemble pruning strategy to reduce weak-learner influence and enhance algorithmic robustness.
- ▸ Evaluations on 35 datasets show consistent superiority over state-of-the-art methods, with significant gains in performance metrics.
Merits
Comprehensive Approach
IMOVNO+ addresses multiple aspects of imbalanced multi-class learning, including data quality and algorithmic robustness, making it a comprehensive solution for this complex problem.
Robustness and Scalability
The framework's use of conditional probability, overlapping-cleaning, and smart oversampling algorithms, combined with ensemble pruning, enables robust and scalable performance on diverse datasets.
Demerits
Computational Complexity
The framework's multi-step approach and use of meta-heuristic pruning may increase computational complexity, potentially limiting its adoption in real-time applications or on resource-constrained devices.
Parameter Tuning
The framework's performance may be sensitive to parameter tuning, which can be time-consuming and requires expertise, potentially limiting its adoption in practice.
Expert Commentary
IMOVNO+ is a significant contribution to the field of imbalanced multi-class learning, offering a comprehensive and robust framework for addressing this complex problem. The framework's use of conditional probability, overlapping-cleaning, and smart oversampling algorithms, combined with ensemble pruning, demonstrates a deep understanding of the challenges and limitations of existing methods. While the framework's computational complexity and parameter tuning requirements may pose challenges, its potential applications in real-world domains make it an exciting development. As the field continues to evolve, IMOVNO+ will likely serve as a benchmark for future research, pushing the boundaries of what is possible in imbalanced multi-class learning.
Recommendations
- ✓ Future research should focus on optimizing the framework's computational complexity and parameter tuning requirements to make it more accessible to practitioners.
- ✓ The framework's performance should be evaluated on a wider range of datasets and applications to further establish its robustness and scalability.
