适用于不平衡数据集分类的改进SVM算法

在分析了传统支持向量机(SVM)对不平衡数据的学习缺陷后,提出了一种改进SVM算法,采用自适应合成(ADASYN)采样技术对数据集进行部分重采样,增加少类样本的数量;对不同的样本点分配不同的权重,减弱噪声对训练结果的影响;使用基于代价敏感的SVM算法训练,缓解不平衡数据对超平面造成的偏移.选择UCI数据库中的6组不平衡数据集进行测试,实验结果表明:在各个数据集上改进SVM算法的性能优于其他算法,并在少类准确率和多类准确率上取得了很好的平衡.

Improved SVM algorithm for imbalanced dataset classification

An improved support vector machine(SVM)algorithm is proposed,after analyzing the deficiency of traditional SVM algorithm for imbalanced datasets.It uses adaptive synthetic(ADASYN)sampling technology for partially resampling on dataset,to increase minority class instances;distribute different weights for different sample point to decrease the influence of noise on training result,cost-sensitive SVM algorithm training is adopted to relieve the bias of hyperplane caused by imbalanced datasets. The proposed algorithm is tested on 6 sets of imbalanced datasets from UCI database. The experimental result shows that the performance of improved SVM algorithm is better than other algorithms and achieve a good balance between minority class accuracy and majority class accuracy.