基于ODR和BSMOTE 结合的不均衡数据SVM分类算法

针对传统的支持向量机（SVM）算法在数据不均衡的情况下分类效果不理想的缺陷,为了提高SVM算法在不均衡数据集下的分类性能,提出一种新型的逐级优化递减欠采样算法.该算法去除样本中大量重叠的冗余和噪声样本,使得在减少数据的同时保留更多的有用信息,并且与边界人工少数类过采样算法相结合实现训练样本数据集的均衡.实验表明,该算法不但能有效提高SVM算法在不均衡数据中少数类的分类性能,而且总体分类性能也有所提高.

SVM classifier for unbalanced data based on combination of ODR and BSMOTE

The classification result of classical support vector machine(SVM) algorithm in the case of unbalanced data
set is not satisfactory. In order to improve the SVM algorithm’s classification performance under unbalanced data set,
a novel under-sampling algorithm based on optimization of decreasing reduction(ODR) is presented. The algorithm is
applied to under-sample the majority class instances for removal of a large number of overlapping samples of redundant
and noise samples, which consequently makes reservations for the majority class instances with more useful information,
and the ODR under-sampling algorithm is combined with border synthetic minority over-sample technique(BSMOTE) to
achieve a balanced training sample data set. The experimental results show that the proposed method can not only improve
classification performance of SVM in the minority class data, but also increase the overall classification performance.a