## 2 Virtual Sample Generation technique  
As mentioned above, the main goal of MTD, FVP, and MVN is generating virtual samples. Virtual Sample Generation (VSG) is a data preprocessing technique proposed to increase the prediction accuracy for the small-dataset issue [27]. This idea was first proposed in 1998 by [28] to improve the recognition (object and speech recognition) performance for image and sound datasets. Another work [22] used the bootstrap method to generate virtual samples in order to enhance the accuracy of a computerized algorithm derived from a small clinical sample in predicting radiotherapy outcomes. Results showed that when the amount of training data and learning accuracy were directly correlated to the prediction, the outcome of radiotherapy increased stably from 55%-85%. The study published in [29] developed a novel technique, based on random variate generation, that was used in the early phase of data gathering to handle a DNA microarray categorization issue. This technique was employed to search for discrete collections in the DNA microarray and to consider outliers as various sets in a nonlinear method. This technique generated virtual samples from the original small datasets based on binary classification. Moreover, the UCI database [30] experiments were used in this study. Finally, the results showed that this method largely enhanced the accuracy of machine learning in the early stages of DNA microarray data, and it significantly helped in terms of solving the problem of the extremely small training dataset with nonlinear data or outliers. The work in [31] proposed a method based on a generative adversarial network put together with a deep neural network. The generative adversarial network was trained with the training group to generate virtual sample data, which increased the training group. Then, the deep neural network classifier was trained with the virtual samples. After that, they tested the classifier with the original test group, and the indicators validated the effectiveness of the method for multi-classification with a small sample size. As an experimental case, the method was then used to recognize the stages of cancers with a small classified sample size. The empirical results verified that the proposed method achieved better accuracy than traditional methods. The study [32] aimed to generate a synthetic sample that reflected the attributes of the people listed in the \"Health Survey for England\". They used data from the \"Health Survey for England\" to define gender and the age-dependent distributions of continuous variable risk factors such as weight, height, number of cigarettes/day, and units of alcohol/week and the prevalence of binary risk factors such as diabetes and smoking status. Spearman rank correlations including gender and age were defined for these risk variables. A table of normally-distributed random numbers was produced. The sample was then generated utilizing a reverse lookup of the gamma distribution value using the random percentiles for continuous variables or setting a binary variable to one when the random percentile fell below the prevalence threshold. The new method produced big virtual samples with risk factor distributions very closely matching those of the real \"Health Survey for England\" population. This sample can be utilized to model the likely impact of new therapies or predict mortality. The work published in another paper [5] developed a new method of VSG called genetic algorithm-based virtual sample generation, which was based on the integrated effects and restrictions of data attributes. The first procedure determined the appropriate range by utilizing MTD functions. Then, a genetic algorithm was applied to accelerate the generation of the most appropriate virtual samples. The last step was verification of the performance of the proposed method by comparing the results with two different forecasting models.