UGRWO-Sampling: A modified random walk under-sampling approach based on graphs to imbalanced data classification

In this paper, we propose a new RWO-Sampling (Random Walk Over-Sampling) based on graphs for imbalanced datasets. In this method, two figures based on under-sampling and over-sampling methods are introduced to keep the proximity information, which is robust to noises and outliers. After the construction of the first graph on minority class, RWO-Sampling will be implemented on selected samples, and the rest of them will remain unchanged. The second graph is constructed for the majority class, and the samples in a low-density area (outliers) are removed. In the proposed method, examples of the majority class in a high-density area are selected, and the rest of them are eliminated. Furthermore, utilizing RWO-sampling, the boundary of minority class is increased though, the outliers are not raised. This method is tested, and the number of evaluation measures is compared to previous methods on nine continuous attribute datasets with different over-sampling rates. The experimental results were an indicator of the high efficiency and flexibility of the proposed method for the classification of imbalanced data.

Weighted second-order cone programming twin support vector machine for imbalanced data classification

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.