## 4 Results
### Experimental Data Description  
The datasets used in the experiments are presented next. All nine datasets were taken from the UCI machine learning repository, are representative for classification problems, and are from various research domains. The datasets were preprocessed for the proposed methodology as follows:  
1. Smart Grids Data: The original data is the Electrical Grid Stability Simulated Data Dataset. The dataset was generated for a smart grid with one producer and three consumers, and the features describe the reaction time of the participants, the power consumed/produced, and the gamma coefficients of the price elasticity. The 13 th feature, which represents the maximal real part corresponding to the characteristic equation root, is removed. The 14 th feature is represented by the labels column. The values'stable' and 'unstable' are converted into 1 and 0, respectively. 2. Raisin Data [39]: This dataset contains information from images of Besni and Kecimen raisin varieties that were grown in Turkey. The features describe the area, perimeter, major axis length, minor axis length, eccentricity, convex area, and extent. The labels \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Configuration** \\ \hline RF & 10 estimators, 42 random state \\ SVM & 0.00001 tolerance, 42 random state \\ GBT & 10 estimators, 1.0 or 0.001 learning rate \\ LR & default parameters \\ K-NN & 9 neighbors \\ NB & default or 100,000 var smoothing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classifier Configurations. column is represented by the 'class' column. The labels 'Kecimen' and 'Besni' were converted into 0 and 1, respectively. 3. Turkish Music Emotion Data [40]: The data are developed as a discrete model. The four classes of this data are 'happy','sad', 'angry', and'relaxed'. The dataset is prepared using non-verbal music and verbal music from various genres of Turkish music. The labels 'happy','sad', 'angry', and'relaxed' are converted into 1, 2, 3, and 0, respectively. 4. Diabetes Risk Prediction Data [41]: The original data are from the Early Stage Diabetes Risk Prediction Dataset. The features contain information about the age of the monitored subjects and various conditions such as obesity, visual blurring, and delayed healing. The labels column is called 'class', and the two types of possible labels are 'positive' and 'negative'. The categorical values are converted to numerical values as follows: 'female', 'yes', and 'positive' are converted to 1, while'male', 'no', and 'negative' are converted to 0. 5. Rice Data [42]: The Rice (Cammeo and Osmancik) Data Set contains data about the Osmancik and Cammeo species of rice. For each grain of rice, seven morphological features were extracted, namely, the area, perimeter, major axis length, minor axis length, extent, convex area, and eccentricity. The 'class' column represents the labels. The values 'Cammeo' and 'Osmancik' were converted into 0 and 1, respectively. 6. Parkinson's Disease Data [43]: The Parkinson's Disease Classification Data Set was gathered from 108 patients with Parkinson's Disease, as follows: 81 women and 107 men. The 64 healthy individuals are represented by 41 women and 23 men. The column 'id' was removed in the preprocessed data. 7. Cervical Cancer Data: The Cervical Cancer (Risk Factors) Data Set contains data which were collected at the Hospital Universitario de Caracas in Caracas, Venezuela. The data were collected from 858 patients, and describe demographic information, historical medical records, and habits. The missing values were replaced using the mean heuristic. The four target variables 'Hinselmann', 'Schiller', 'cytology', and 'biopsy' were converted into a single target variable equal to \(2^{3}\times Hinselmann+2^{2}\times Schiller+2^{1}\times Cytology+2^{0}\times Biopsy\). This conversion led to the following set of labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15}.