## 1 Introduction
Oversampling is to repeat positive proportional data for categories with small sample numbers, and undersampling is to discard a portion of the data for sample species with a larger number of samples, thus making the number of samples under each category equal, such as with the K-nearest neighbor (KNN) algorithm and boosting algorithms [6]. Traditional oversampling techniques use random sampling, which simply copies the sample data to make the number of samples increase rapidly. This approach may cause overfitting problems: as the number of replicates of the samples increases, the error information in the samples will also be amplified, thus making the model in the learning process continue to fit the error information. The information learned from such models is too specific and insufficient, thus it will appear  
Figure 1: The representation of an unbalanced 2D dataset in 2D coordinates. that the training set works well, but the test set works poorly [7]. In this paper, the SMOTE algorithm as improved based on the stochastic process is used. Its basic idea is to analyze the minority class samples and synthesize new samples via manual interpolation based on the characteristics of the minority class samples to increase the balance of the dataset. Figure 2 shows the process of a random selection of proximal points by using the SMOTE algorithm and the process of randomly generating new samples at the proximity point using the SMOTE algorithm. Support vector machine theory first came from statistics as a binary linear classifier. In 1992, kernel methods were proposed by [PERSON], [PERSON], and [PERSON]. The introduction of kernel functions made support vector machine theory available for solving nonlinear classification problems [8]. Support vector machines are essentially binary classifiers, and when dealing with multiclassification problems, their algorithms must be improved. Currently, there are two methods for multiclassification using support vector machines: the direct method and the indirect method. The direct method directly modifies the objective function and combines the parameter solutions of multiple classification surfaces into one optimization problem and achieves multiclass classification by solving that optimization problem at once. This method is simple in principle but not easy to implement, and it is only applicable to small- and medium-sized problems and cannot be applied on a large scale. Indirect methods, i.e., constructing multiple binary solvers to transform a multiclassification problem into multiple binary problems, are more widely used, such as the one-vs-one method, the one-vs-all method, the derived directed acyclic graph method, and the binary tree method [9; 10; 11]. The one-vs-one method constructs a binary classifier in any two classes of training samples, and for the \(n\)-class classification problem, the one-vs-one method constructs \(C_{n}^{2}\) binary classifiers. The one-vs-all method treats each class of samples as one positive sample and the remaining samples as negative samples, thus for the \(n\)-class classification problem, the one-vs-all method must only construct \(n\) binary classifiers. Compared with the one-vs-one method, the computational effort of the one-vs-all method is much smaller than that of the one-vs-one method, but there are certain disadvantages of the one-vs-all method in solving the classification problem, such as the unbalanced number of positive and negative samples, the need to train all models when adding new categories or other elements, and the existence of some areas in which the samples within its range meet two or more hyperplane equations at the same time. Figure 3 show the classification schematics of the same set of two-dimensional data under the one-vs-one and one-vs-all methods, respectively. Under the premise of classification by using the one-vs-all method, there exist two classes of transition areas between hyperplanes [12; 13].