中文摘要：

不平衡数据的分类问题是机器学习研究领域的重要问题，有着广泛的应用，如软件模块缺陷检测．基于支持向量机的不平衡数据分类方法是主流的分类方法之一，受到研究者广泛的关注．本文在已有的基于模糊支持向量机的不平衡数据分类方法的基础上，结合抽样技术，提出了基于模糊支持向量机的不平衡数据分类算法和基于模糊支持向量机的不平衡数据分类集成算法．在NASA的两个软件模块缺陷度量数据集CM1和KC3上的实验结果表明了本文新提出算法的有效性．

英文摘要：

Classification problem on imbalanced data is a key issue in the machine learning field, obtaining data is unbalanced in many real applications, such as the defect prediction for software modules. The classification methods based on support vector machine for imbalanced data is one of the effective classification approaches, many researchers focus on these methods. Due to the software modules defect metric datasets have the characteristics, such as class imbalance and noise, the prediction models based on the normal support vector machine （SVM） can＇t get satisfactory results. Therefore, in this paper, we make a relatively in-depth study on support vector machine for predicting software module defects. Based on the previously proposed fuzzy support vector machine for imbalanced data classification （FSVM_CIL）, integrating sampling technology, in this paper we introduce two improved algorithms： One is FSCM_CIL_RUS, which combines FSVM_CIL algorithm with random under sampling algorithm. Before building software module defect prediction models using FSVM_CIL, we balance the datasets using random under sampling. And the other is an ensemble algorithm called FSVM_CIL RBBag. This algorithm combines the FSVM_CIL algorithm with roughly balanced bagging algorithm. Using FSVM_CIL algorithm to build base classifiers, and then we ensemble the base classifiers to improve the prediction performance. Appling two algorithms to two NASA software module datasets CM1 and KC3, and experimental results show the effectiveness of the newly proposed algorithms, and the combination between FSVM_CIL and the sampling technology or ensemble technology can improve the performance of the module.