【摘要】 类不平衡问题是有监督机器学习领域中的一项重要挑战。在一个不平衡训练集中，虽然少数类的规模显著小于多数类，但少数类往往是人们更为关注的并且比多数类具有更高的误分类代价。大多数分类器算法通常以总体分类精度作为优化目标，容易误分类对分类精度贡献较小的少数类样例。现有不平衡学习方法往往将训练集的类不平衡比例IR作为分类复杂性度量，并将其作为优化目标。然而，最近研究表明，与IR相比，类重叠更能客观度量不平衡数据的学习难度。鉴于类重叠这一数据复杂性指标的重要性，研究从类重叠视角解决不平衡问题，并提出一种基于类重叠信息的不平衡数据学习方法FO-RBU。具体地，利用各属性上类重叠样例的比例分布来衡量不平衡数据的学习难度，并将其作为确定径向欠采样方法RBU合适欠采样程度的理论依据。实验结果表明，基于属性值的类重叠信息能较好地指导合适欠采样比例的确定，并且提出的类不平衡学习方法FO-RBU是有效的。更多还原

【Abstract】 Class imbalance problem is an important challenge in supervised machine learning field.In an imbalanced training set, although the minority class is significantly outnumbered by the majority class, it usually attracts more attention from the practitioners and has higher misclassification cost than the latter one.Most classifier learning algorithms usually employ the overall classification accuracy as the optimization goal, and thus easily misclassify the minority class examples that make less contribution to overall classification accuracy.Existing imbalance learning approaches often utilize the class imbalance ratio(IR) of a training set as the classification complexity measure as well as the optimization goal.However, it has recently been indicated that, compared with IR,class overlap can more objectively measure the learning difficulty of an imbalanced dataset.Considering the importance of class overlap in evaluating the data complexity, the imbalance problem is solved from the class overlap perspective, and an imbalanced dataset learning approach FO-RBU utilizing the class overlap information of a training set is proposed.Specifically, the distribution concerning the ratios of feature based class overlap examples is employed to evaluate the learning difficulty of an imbalanced dataset, and further utilized as a theoretical guideline in determining the proper undersampling extent of Radial-Based Undersampling approach.Experimental results show that the feature values based class overlap information is a good indicator in the proper undersampling ratio determination process, and the proposed class imbalance learning approach FO-RBU is effective.更多还原