## 4 Experiments
### Results and Discussion  
Table 3 presents the classification performance of the _CR1_, _CR2_, k-NN, RF, SVM, MLP, NB, and NC classifiers. The results are based on the average AUC obtained from five repetitions of 5-fold cross-validation. For the _CR1_ classifier, which utilizes mean and standard deviation-based reference intervals, we applied \(t=1.1\) standard deviations. For \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Attribute** & **Class** & **Instances** \\ \hline Dermatology & 34 & 6 & 366 \\ \hline HeartDisease & 13 & 5 & 303 \\ \hline Thyroid & 20 & 2 & 720 \\ \hline Pageblocks & 10 & 4 & 548 \\ \hline Breast Cancer & 9 & 2 & 699 \\ \hline Dermatology & 34 & 4 & 366 \\ \hline Newthyroid & 5 & 3 & 215 \\ \hline Geobia & 147 & 9 & 169 \\ \hline Glass & 9 & 6 & 214 \\ \hline Leaf & 14 & 30 & 340 \\ \hline Sonar & 60 & 2 & 208 \\ \hline SPECTF & 44 & 2 & 267 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptions of the datasets used in our experiments. the _CR2_ classifier, which employs percentile-based reference intervals, we used the 90 th percentile (\(U=0.9\)) as the upper bound and the 10 th percentile (\(L=0.1\)) as the lower bound. The best classification performance for each dataset is highlighted in boldface. Based on the experimental study presented in Table 3, _CR1_ (percentile-based) achieved the highest average AUC, closely followed by RF and _CR2_ (mean and standard deviation-based). As observed in Table 3, both _CR1_ and _CR2_ consistently outperform other classifiers including NB, SVM, k-NN, MLP, and NC. Notably, _CR1_ (percentile-based) achieved the highest average AUC among all classifiers. Table 4 presents a detailed analysis of the comparative performance of the developed classifiers, _CR1_ and _CR2_, alongside other established classifiers, using average normalized AUC scores as the evaluation metric. The ranking process for the normalized scores was carried out in the following systematic steps:  
1. Metric calculation: * Compute the AUC for each classifier on the test set of each dataset. * Calculate the average AUC across all datasets for each classifier. 2. Score normalization: Normalize the AUC scores using the min-max normalization technique, ensuring all scores are on a comparable scale. 3. Average normalized scores: Determine the average normalized score for each classifier by taking the mean of the normalized scores across all datasets. 4. Algorithm ranking: Rank the classifiers based on their average normalized scores, with higher scores indicating better performance. Furthermore, the classifiers were grouped into high-, medium-, and low-performance categories (Table 5) based on their normalized scores, as shown in Table 4. This grouping concept was inspired by Jenks Natural Breaks Optimization, which seeks to identify natural divisions in data by minimizing intra-group variance and maximizing inter-group variance [41; 42]. While [PERSON] method provided the conceptual framework, the actual grouping in this study was simplified by employing percentile-based thresholds. The classifiers were divided into thirds for enhanced clarity and interpretability.