  We propose a method of using a Weighted second-order cone programming twin
support vector machine (WSOCP-TWSVM) for imbalanced data classification. This
method constructs a graph based under-sampling method which is utilized to
remove outliers and reduce the dispensable majority samples. Then, appropriate
weights are set in order to decrease the impact of samples of the majority
class and increase the effect of the minority class in the optimization formula
of the classifier. These weights are embedded in the optimization problem of
the Second Order Cone Programming (SOCP) Twin Support Vector Machine
formulations. This method is tested, and its performance is compared to
previous methods on standard datasets. Results of experiments confirm the
feasibility and efficiency of the proposed method.
