Stochastic Gradient Descent with SVM for Imbalanced Data Classification
Journal Title: Scholars Journal of Physics, Mathematics and Statistics - Year 2016, Vol 3, Issue 4
Abstract
Stochastic Gradient Descent (SGD) is an attractive choice for SVM training. SGD leads to a result that the probability of choosing majority class is far greater than that of minority class for imbalanced classification problem. In order to deal with the large-scale imbalanced data classification problems, a method named stochastic gradient descent algorithm with SVM for imbalanced data classification is proposed. First, to deal with imbalanced data classification problems, we define the weight according to the size of positive and negative dataset. Then, a fast learning algorithm on large datasets called the weighted stochastic gradient descent algorithm with SVM is proposed, which helps to reduce the hyperplane offset to the minority class, thus solve the large-scale imbalanced data classification problems. Experimental results on real datasets show that the proposed method is effective.
Authors and Affiliations
Lu Shuxia, Zhu Chenxu, Zhou Mi
Keywords
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