Class imbalance and noise present significant challenges in numerous real-world classification tasks. The prevalence of an uneven distribution of samples typically results in a bias towards the majority class in Support Vector Machine (SVM) classifiers, compounded by the often inherent noise within these samples. Addressing both class imbalance and noise, we introduce two fuzzy-based methodologies. The first method employs intuitionistic fuzzy membership, resulting in the development of the Robust Energy-based Intuitionistic Fuzzy Least Squares Twin Support Vector Machine (IF-RELSTSVM), a model specifically designed for class imbalance learning. The IF-RELSTSVM model is distinguished by its use of intuitionistic fuzzy scores for both classes, significantly attenuating the detrimental effects of noise and outliers. A distinctive attribute of IF-RELSTSVM is its proficiency in processing noisy data points, whether proximate to or distant from the hyperplane. Additionally, we introduce a novel concept of hyperplane-based fuzzy membership, calculating fuzzy memberships through a projection-based approach. This foundation supports the formulation of a Robust Energy-based Fuzzy Least Squares Twin Support Vector Machine (F-RELSTSVM), also aimed at class imbalance learning. The efficacy of the proposed IF-RELSTSVM and F-RELSTSVM algorithms is rigorously evaluated across several benchmark and synthetic datasets, employing the Area Under the ROC Curve (AUC) as a performance metric. Experimental findings indicate that these algorithms surpass baseline models in the majority of datasets tested. Statistical analyses further validate the significance of our proposed methods, demonstrating their suitability for application in environments characterized by noise and class imbalance. A case study in credit card fraud detection showcases the F-RELSTSVM algorithm achieving an impressive average AUC of 90.84%, thereby outperforming comparable algorithms and highlighting the practical applicability of our approaches in tackling challenging datasets.

中文翻译：

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增强类不平衡解决方案：基于投影的模糊 LS-TSVM 方法


类不平衡和噪声在许多现实世界的分类任务中提出了重大挑战。样本分布不均匀的普遍现象通常会导致支持向量机 (SVM) 分类器对多数类别产生偏见，而这些样本中通常存在固有噪声，这使得情况更加复杂。为了解决类别不平衡和噪声问题，我们引入了两种基于模糊的方法。第一种方法采用直觉模糊隶属度，从而开发了基于鲁棒能量的直觉模糊最小二乘双支持向量机（IF-RELSTSVM），这是一种专门为类别不平衡学习而设计的模型。 IF-RELSTSVM 模型的特点是它对两个类别都使用直观模糊分数，显着减弱了噪声和异常值的有害影响。 IF-RELSTSVM 的一个独特属性是它能够熟练地处理噪声数据点，无论是靠近超平面还是远离超平面。此外，我们引入了基于超平面的模糊隶属度的新概念，通过基于投影的方法计算模糊隶属度。该基金会支持制定基于鲁棒能量的模糊最小二乘双支持向量机（F-RELSTSVM），也旨在类不平衡学习。所提出的 IF-RELSTSVM 和 F-RELSTSVM 算法的功效在多个基准和合成数据集上进行了严格评估，采用 ROC 曲线下面积 (AUC) 作为性能指标。实验结果表明，这些算法在大多数测试数据集中超越了基线模型。统计分析进一步验证了我们提出的方法的重要性，证明了它们适用于以噪声和类别不平衡为特征的环境中的应用。 信用卡欺诈检测的案例研究展示了 F-RELSTSVM 算法的平均 AUC 达到了 90.84%，令人印象深刻，从而优于同类算法，并强调了我们的方法在处理具有挑战性的数据集方面的实际适用性。