DBBRBF：采用混合分布基平衡实例选择与径向基函数分类器的软件缺陷预测问题优化方法
DBBRBF- Convalesce optimization for software defect prediction problem using hybrid distribution base balance instance selection and radial basis Function classifier
AI总结：
针对软件缺陷预测数据的类别不平衡问题，提出结合分布基平衡实例选择与径向基函数分类器的DBBRBF方法，在多类不平衡数据集上验证了有效性并通过统计检验确认适用性。
摘要：
随着软件工程的快速发展，软件正成为人类生活中不可或缺的一部分，这要求软件具备极高的可靠性。可借助高效的软件测试方法，利用历史软件预测数据开展可靠性检查，以构建高质量的软件系统。机器学习凭借其有效性，在优化实际软件中易缺陷模块的预测方面发挥着关键作用。软件缺陷预测数据存在类别不平衡问题，即缺陷类与非缺陷类的占比偏低，这亟需高效的机器学习分类技术，否则会降低分类性能。为缓解该问题，本文提出一种新型的基于实例的混合分类方法，将基于分布基平衡的实例选择与径向基函数神经网络分类器模型（DBBRBF）相结合，以获取优于现有研究的最优预测效果。实验分析采用了NASA、Promise和Softlab的类别不平衡数据集。从准确率、F值、AUC、召回率、精确率和平衡度等指标来看，实验结果验证了所提方法的有效性。最后，本文开展了统计显著性检验，以明确所提模型的适用性。
英文摘要：
Software is becoming an indigenous part of human life with the rapid development of software engineering, demands the software to be most reliable. The reliability check can be done by efficient software testing methods using historical software prediction data for development of a quality software system. Machine Learning plays a vital role in optimizing the prediction of defect-prone modules in real life software for its effectiveness. The software defect prediction data has class imbalance problem with a low ratio of defective class to non-defective class, urges an efficient machine learning classification technique which otherwise degrades the performance of the classification. To alleviate this problem, this paper introduces a novel hybrid instance-based classification by combining distribution base balance based instance selection and radial basis function neural network classifier model (DBBRBF) to obtain the best prediction in comparison to the existing research. Class imbalanced data sets of NASA, Promise and Softlab were used for the experimental analysis. The experimental results in terms of Accuracy, F-measure, AUC, Recall, Precision, and Balance show the effectiveness of the proposed approach. Finally, Statistical significance tests are carried out to understand the suitability of the proposed model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
