基于BSMOTE和逆转欠抽样的不均衡数据分类算法

摘要

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.%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。