## 4 Experiment  
### Datasets  
The above-described method was developed using a dataset provided by Scientific Institute IRCCSEugenio Medea. In addition, the proposed technique was tested with other available benchmark datasets. The first dataset was collected at Scientific Institute IRCCS Eugenio Medea in Italy (Bosisio Parini, Italy). The dataset included data from 30 participants divided into two groups: 15 children with a clinical diagnosis of Autism Spectrum Disorder (ASD) (\"ASD is a neurodevelopmental disorder characterised by persistent social impairment, communication abnormalities, and restricted and repetitive behaviours (DSM-5) (1, 2). ASD is a complex condition with an average prevalence of about 1% worldwide.\") and 15 participants with typical development. For each participant, 17 kinematic parameters (numerical features) related to an upper-limb movement were registered [35]. In the original work, a feature selection algorithm was used to identify the seven features that best differentiated participants with ASD from healthy controls. In the present work, our algorithm randomly selected six participants (from now on, samples) as an original small dataset, with three samples for each group (ASD and healthy children), and the remaining 24 samples were used as the testing dataset. For comparison, we generated 10,000 virtual samples using the proposed method for every collection in the first trial, and then, this rose by the following numbers each time: 25,000, 50,000, 100,000, 200,000, and 300,000. The identical learning procedure was repeated for five rounds. The second dataset was the _Escherichia coli_ dataset [30; 36], and this included values of the measurements of _E. coli_ bacteria (commonly found in the human gut). This dataset included 336 samples. Each sample belonged to one of eight classes, where each class referred to a type of _E. coli_ bacteria. Each sample consisted of eight attributes; see the dataset description listed in Table 1. The training and testing set for this dataset were chosen as follows. We selected 77 samples for each of the first two classes (CPand IM) to ensure that we had binary classes with an equal sample number. Then, three samples were selected randomly from each of the selected class. These three samples represented the original small dataset. Starting from these three samples, the proposed method generated 10,000 virtual samples for every class for the first time. Then, the number of virtual samples were increased as follows: 25,000, 50,000, 100,000, 200,000, and 300,000 from each class. To give an example of this, the total number of training samples for the first round was 20,006 (six actual samples plus 20,000 virtual samples). The remaining 148 samples were used as testing data. After that, we repeated the experiment, randomly selecting five samples as an original small dataset, from each class of the _E. coli_ dataset. Doing so, the total number of training samples was 10 plus the virtual samples. The remaining 144 samples were used as testing samples, and these steps were repeated five times. The third dataset was the Breast Tissue dataset [36; 37], and this included values of the measurements relative to the electrical impedance of freshly-excised tissue from the breast. This dataset  
Figure 6: Normal distribution for one feature after generating the virtual sample. included 106 samples where each sample belonged to one of six classes. Six classes of freshly-excised tissue were studied using electrical impedance measurements: carcinoma, fibroadenoma, mastopathy, glandular, connective, and adipose. The training and the testing set for this dataset were chosen as follows. We selected 15 samples for each of the first two classes (carcinoma and fibroadenoma) to ensure that we had binary classes with an equal samples number. Then, three samples were selected randomly from each of the selected classes. These three samples represented the original small dataset.