Abstract: For imbalanced data classification,the performance of using traditional stochastic gradient descent for solving SVM problems is not very well.Adaptive stochastic gradient descent algorithm defines a distribution pinstead of using uniform distribution to choose examples,and the smoothing hinge loss function is used in the optimization problem.Because of the training sets are imbalanced,using uniform distribution will cause the algorithm choose more majority class based on the imbalanced ratio.That would result the classifier bias towards the minority class.The distribution p largely overcomes this issue.When to stop the programs becomes an important problem,because the normal stochastic gradient descent algorithm does not have a stop criterion especially for large data sets.The stop criterion was setted according to the classification accuracy on the training sets or its subsets.This stop criterion could stop the programs very early especially for large data sets if the parameters are chosen properly.Some experiments on imbalanced data sets show that the proposed algorithm is effective.

Key words: Loss function, Nonuniform distribution, Stochastic gradient descent, Stop criterion, Support vector machine