IMOVNO+: 一种用于不平衡多类学习的区域划分与元启发式集成框架
IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning
AI总结：
IMOVNO+通过数据层面的重叠清洁和算法层面的元启发式方法，提升多类学习中不平衡、重叠和噪声问题的处理效果，实现性能显著提升。
摘要：
类别不平衡、重叠和噪声会降低数据质量，减少模型可靠性，并限制泛化能力。尽管在二分类中广泛研究，这些问题是多类设置中仍被低估的问题，其中复杂的类间关系使少数类-多数类结构不明确，传统聚类无法捕捉分布形状。仅依赖几何距离的方法有风险移除信息样本并生成低质量合成数据，而二值化方法只在局部处理不平衡问题，忽略了类间全局依赖关系。在算法层面，集成方法难以整合弱分类器，导致鲁棒性有限。本文提出了IMOVNO+（IMbalance-OVerlap-NOise+算法层面优化），一种两级框架，旨在共同提升二分类和多分类任务的数据质量和算法鲁棒性。在数据层面，首先使用条件概率量化每个样本的信息性。其次，将数据集划分为核心、重叠和噪声区域。第三，引入一种重叠清洁算法，结合Z分数指标与大跳跃间隙距离。第四，基于多正则化的智能过采样算法控制合成样本的接近程度，防止新的重叠。在算法层面，元启发式方法修剪集成分类器以减少弱学习者的影响。IMOVNO+在35个数据集（13个多类，22个二分类）上进行了评估。结果表明，其在多个案例中接近100%的性能。对于多类数据，IMOVNO+在G-mean上获得37-57%的提升，在F1-score上获得25-44%的提升，在精度上获得25-39%的提升，在召回率上获得26-43%的提升。在二分类任务中，其接近完美表现，提升幅度为14-39%。该框架能够处理数据稀缺和不平衡问题，从数据收集和隐私限制中。
英文摘要：
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.
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