IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning

2026-02-22

AI Summary

This study addresses the performance degradation in multi-class classification caused by class imbalance, inter-class overlap, and noise coupling. The authors propose IMOVNO+, a two-layer framework that operates at both data and algorithmic levels. At the data level, samples are partitioned into core, overlapping, and noisy regions based on conditional probability; overlapping instances are cleaned using a combination of Z-score and big-jump gap distance, while a multi-regularized intelligent oversampling strategy is designed to avoid introducing new overlaps. At the algorithmic level, a metaheuristic-based pruning strategy enhances the robustness of ensemble classifiers. IMOVNO+ uniquely integrates conditional probability-driven region partitioning, distribution shape-aware overlap cleaning, multi-regularized oversampling, and metaheuristic optimization. Evaluated on 35 datasets, the method significantly outperforms existing approaches, improving multi-class G-mean by 37–57% and F1-score by 25–44%.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Intelligent Robots: Multi-Robot Systems

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• class imbalance
• overlap
• noise
• multi-class learning
• data quality

Innovation

• imbalanced multi-class learning
• regional partitioning
• meta-heuristic ensemble
• smart oversampling
• overlap cleaning