摘要
针对传统分类器在数据不均衡的情况下分类效果不理想的缺陷,为提高分类器在不均衡数据集下的分类性能,特别是少数类样本的分类能力,提出了一种基于BSMOTE和逆转欠抽样的不均衡数据分类算法。该算法使用BSMOTE进行过抽样,人工增加少数类样本的数量,然后通过优先去除样本中的冗余和噪声样本,使用逆转欠抽样方法逆转少数类样本和多数类样本的比例。通过多次进行上述抽样形成多个训练集合,使用Bagging方法集成在多个训练集合上获得的分类器来提高有效信息的利用率。实验表明,该算法较几种现有算法不仅能够提高少数类样本的分类性能,而且能够有效提高整体分类准确度。
The result of classical classification algorithms in the case of imbalanced data sets is not satisfactory.In order to im-prove the classification performance under imbalanced data sets,especially the classification ability of the minority class,this pa-per presented a novel classification algorithm for imbalanced data sets based on combination of border synthetic minority oversam-pling technique （BSMOTE）and inverse under sampling.It used BSMOTE to increase the sample number of minority class,and then used a inverse under sampling method to inverse the cardinalities of the majority and minority class ratio through removing the samples of redundant and noise sample firstly.By sampling several times,it created a large number of distinct training sets.It used Bagging method to ensemble the classifiers trained on those data sets to improve the efficient use of the original data sets.Ex-perimental results show that the proposed algorithm can not only improve classification performance in the minority class data,but also increase the overall classification accuracy rate effectively than several existing algorithms.
出处
《计算机应用研究》
CSCD
北大核心
2014年第11期3299-3303,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61004069)
安徽省自然科学基金资助项目(1208085QF107)
