计算机科学与探索2017，Vol.11Issue(10)：1662-1671,10.DOI:10.3778/j.issn.1673-9418.1609009
非均衡加权随机梯度下降SVM在线算法
Imbalanced Weighted Stochastic Gradient Descent Online Algorithm for SVM
摘要
Abstract
Stochastic gradient descent (SGD) has been applied to large scale support vector machine (SVM) training. Stochastic gradient descent takes a random way to select points during training process, this leads to a result that the probability of choosing majority class is far greater than that of choosing minority class for imbalanced classifica-tion problem. In order to deal with large scale imbalanced data classification problems, this paper proposes a method named weighted stochastic gradient descent algorithm for SVM. After the samples in the majority class are assigned a smaller weight while the samples in the minority class are assigned a larger weight, the weighted stochastic gradi-ent descent algorithm will be used to solving the primal problem of SVM, which helps to reduce the hyperplane off-set to the minority class, thus solves the large scale imbalanced data classification problems.关键词
随机梯度下降(SGD)/权/非均衡数据/大规模学习/支持向量机(SVM)Key words
stochastic gradient descent (SGD)/weight/imbalanced data/large scale learning/support vector ma-chine (SVM)分类
信息技术与安全科学引用本文复制引用
鲁淑霞,周谧,金钊..非均衡加权随机梯度下降SVM在线算法[J].计算机科学与探索,2017,11(10):1662-1671,10.基金项目
The Natural Science Foundation of Hebei Province under Grant No. F2015201185 (河北省自然科学基金). （河北省自然科学基金）
