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Imbalanced Stochastic Gradient Descent Online Algorithm For SVM

Posted on:2018-11-17Degree:MasterType:Thesis
Country:ChinaCandidate:M ZhouFull Text:PDF
GTID:2348330539485815Subject:Master of Engineering - Software Engineering
Abstract/Summary:PDF Full Text Request
Stochastic Gradient Descent(SGD)has been applied to large scale 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 minority class for imbalanced classification problem.In order to deal with large scale imbalanced data classification problems,a method named Weighted Stochastic Gradient Descent Algorithm for SVM is proposed.After the samples in the majority class are assigned a smaller weight while the samples in the minority class are assigned a larger weights,the weighted stochastic gradient descent algorithm will be used to solve the primal problem of SVM,which helps to reduce the hyperplane offset to the minority class,thus solve the large scale imbalanced data classification problems.At the same time,the proposed algorithm can improve the accuracy and stability of the classification effect of imbalanced data by using the Log-loss function,different kinds of weighting method and using mini-batch gradient descent algorithm.
Keywords/Search Tags:Stochastic gradient descent, Weight, Unbalanced data, Support vector machines, Loss function
PDF Full Text Request
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