## 2 Materials and Methods
### Preprocessing  
Once the instances were defined, we applied a normalization method. For this work, we decided on the normalization method to be used to transform each dataset. We used a normalization; this process rescales data to a specific range, typically between 0 and 1 or \(-1\) and 1, ensuring a consistent scale across feature. Moreover, standardization adjusts data by centering them around a mean of 0 and scaling them to have a standard deviation of 1. This technique preserves the original distribution and is particularly effective when working with normally distributed data.  
For the selection of the normalization method, each attribute of each dataset was reviewed to evaluate which type of standardization would be applied, considering if the attributes presented a normal distribution after we applied standardization, and, in cases where the data needed rescaling, we applied normalization.  
Table 2 shows a sample of the results of the analysis performed for the selection of the normalization technique for all datasets, where columns show the attribute number of the dataset, the statistical information for each attribute (minimum value, maximum value, mean, and standard deviation), and the decision taken to normalize each attribute, where the norm is obtained from Equation (1), std is calculated with Equation (2), and the \(-\) symbol indicates that no normalization method was required for this attribute.  
\[Norm(x_{i})=\frac{x_{i}-min_{val}}{max_{val}-min_{val}} \tag{1}\]  
\[Std(x_{i})=\frac{x_{i}-\overline{x}}{\sigma} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attribute** & **Min** & **Max** & **Mean** & **Standard Deviation** & **Decision** \\ \hline banknote\_auth & & & & & \\ \hline a1 & \(-\)7.0421 & 6.8248 & 0.43373526 & 2.84172641 & Std \\ a2 & \(-\)13.7731 & 12.9516 & 1.92235312 & 5.86690749 & Norm \\ a3 & \(-\)5.2861 & 17.9274 & 1.39762712 & 4.30845909 & Std \\ a4 & \(-\)8.5482 & 2.4495 & 1.19165652 & 2.10024732 & Std \\ \hline Iris & & & & & \\ \hline a1 & 4.3 & 7.9 & 5.84333333 & 0.82530129 & Norm \\ a2 & 2 & 4.4 & 3.054 & 0.43214658 & Norm \\ a3 & 1 & 6.9 & 3.75866667 & 1.75852918 & Norm \\ a4 & 0.1 & 2.5 & 1.19866667 & 0.76061262 & Norm \\ \hline pulsar\_stars & & & & & \\ \hline a1 & 5.8125 & 192.617188 & 111.079968 & 25.6522187 & Std \\ a2 & 24.7720418 & 98.7789107 & 46.5495316 & 6.84299824 & Std \\ a3 & \(-\)1.87601118 & 8.06952205 & 0.47785726 & 1.06400999 & Std \\ a4 & \(-\)1.79188598 & 68.1016217 & 1.770279 & 6.16774094 & Std \\ a5 & 0.2132107 & 223.392141 & 12.6143997 & 29.4720738 & Std \\ a6 & 7.37043217 & 110.642211 & 26.3265147 & 19.4700284 & Std \\ a7 & \(-\)3.13926961 & 34.5398442 & 8.30355612 & 4.50596597 & Std \\ a8 & \(-\)1.9769756 & 1191.00084 & 104.857709 & 106.511564 & Std \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attribute information sample for preprocessing.  
In addition, we perform an Artificial Neural Network with the TensorFlow library in Python to have an accuracy reference value. The Artificial Neural Network structure for this experiment was an input layer with the number of neurons equal to the number of attributes, a hidden layer with the same number of neurons as the input layer, and an output layer with one neuron. Each dataset was divided into training and testing. The production percentages of each set in the experimentation were 70% training and 30% test.