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.

QLMC-HD: Quasi Large Margin Classifier based on Hyperdisk

In the area of data classification, the different classifiers have been developed by its own strengths and weaknesses. Among these classifiers, we propose a method which is based on the maximum margin between two classes. One of the main challenges in this area is dealt with noisy data. In this paper, our aim is to optimize the method of large margin classifier based on hyperdisk (LMC-HD) and incorporate it into quasi-support vector data description (QSVDD) method. In the proposed method, the bounding hypersphere is calculated based on the QSVDD method. So our convex class model is more robust compared with support vector machine (SVM) and less tight than LMC-HD. Large margin classifiers aim to maximize the margin and minimizing the risk. Sine our proposed method ignores the effect of outliers and noises, so this method has the widest margin compared with other large margin classifiers. In the end, we compare our proposed method with other popular large margin classifiers by the experiments on a set of standard data which indicates our results are more efficient than the others.