## 3 Methods and Materials
### Statistical Hypothesis Tests  
The use of statistical hypothesis testing in comparisons of classification algorithms implies an analysis complement between them, indicating whether one algorithm is better than another in a specific task and determining the probability of incorrectly detecting a statistical difference when there is no difference [22]. One of the goals of these tests is to verify the truth of the null hypothesis, which is the statement that there is no distribution difference between samples (datasets). Thus, the hypothesisverified is H0 (valid and not rejected) or H1 (not valid and rejected) [23]. There are different types of statistical hypothesis testing, namely:  
1. Normality testing that is used to evaluate the assumption of a sample taken from a distributed population [24];
2. Correlation test that analyzes sample datasets to identify if two variables are related to each other [25];
3. Association test that reports on the relationship of the statistical association between variables [26];
4. Variance test comparing the means of different populations [27];
5. Central tendency test that uses central tendency measures (arithmetic mean, median) to test a probability distribution [28]. From the relationship of the described test types, the central tendency test is the most suitable for the development of the work, because it uses the classification accuracy metric as a measure of central tendency. Table 2 presents the central tendency tests. There are different characteristics among the types of tests presented in Table 2, as follows:  
1. Categorization indicates whether the test is parametric or nonparametric. Parametric tests evaluate the null hypothesis from specific data or parameters (mean, standard deviation, etc.). Nonparametric tests evaluate the null hypothesis from distribution types and group relationships [29];
2. Variable indicates the types of variables the test supports;
3. Group, which matches whether the group comparison is individual, paired, or multiple. In this context the classification algorithms are the groups;
4. Pairing, which corresponds to whether it is paired or unpaired. Paired tests match that the data used for predictor model training are also used to test the predictor model, whereas unpaired tests use one dataset for training and another for testing [30]. Based on the characteristics of the statistical hypothesis tests, we listed those that fit the experiments performed in Section 5, as follows: nonparametric, quantitative, multiple, and paired. The nonparametric characteristic was selected, as it was necessary to identify whether there is really a statistical difference in classification performance between classification algorithms. The classification accuracy metric coincides with the quantitative variable characteristic. Therefore, it is necessary to use multiple comparison tests because the comparison uses six algorithms. Finally, paired tests are best suited, as a single artificial dataset is used for training and testing. Therefore, the Friedman test with these characteristics was listed, to be applied for the comparison of classification algorithms in the Section 5.4 classification precision metric. By testing the null hypothesis, it is possible to find out if datasets are different from each other, but it is not possible to identify which ones are. Therefore, to solve this impasse, the Friedman test is used, which performs multiple comparisons between equal-sized datasets analyzing the variance and randomization between them. The comparison is made from a ranking presented in Figure 3. To implement, it is necessary to transform raw data into ordered data [31]. In the context of classification algorithms and datasets, \(x_{bk}\) represents the placement that the algorithm obtained relative to the dataset in the ranking. This matches that each ranking row corresponds to the random seed value with which the dataset was shuffled and each column corresponds to the algorithm that was applied.