ABSTRACT Classification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution for classifying imbalanced data and diminishing the effect of outliers. The proposed method combines the benefits of “structured” learning models (such as structural support vector machine) with the advantages of “cost-sensitive” learning models (such as weighted relaxed support vector machine). We call our method twin structural weighted relaxed support vector machine (TS-WRSVM). A TS-WRSVM uses two nonparallel hyperplanes to determine the class label of new data so that each model only considers the structural information of one class. We allocate a weight and a limited amount of penalty-free slack to each model by considering the size of each class. Results of experiments indicate that a TS-WRSVM is superior to other current algorithms based on cost-sensitive learning in the areas of classification accuracy and computational time.

中文翻译：

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TS-WRSVM：双结构加权松弛支持向量机

摘要 具有不平衡类分布的数据分类是数据挖掘社区的一个主要问题。在存在异常值的情况下，不平衡分类是一项具有挑战性的任务。在本文中，我们针对数据分布的结构提出了一种新的成本敏感学习方法，用于对不平衡数据进行分类并减少异常值的影响。所提出的方法结合了“结构化”学习模型（如结构支持向量机）的优点和“成本敏感”学习模型（如加权松弛支持向量机）的优点。我们称我们的方法为双结构加权松弛支持向量机（TS-WRSVM）。TS-WRSVM 使用两个不平行的超平面来确定新数据的类标签，因此每个模型只考虑一个类的结构信息。我们通过考虑每个类的大小为每个模型分配一个权重和有限数量的无惩罚松弛。实验结果表明，TS-WRSVM 在分类精度和计算时间方面优于其他当前基于成本敏感学习的算法。