## 5 Results  
Tables 7 and 8 show the results for the best configuration of CEE, NLCEE, and BIN-NLCEE for all datasets and their classes. Since the methods based on GA run \(n\) times for \(n\) classes, and the datasets used in this work are binary classification, the results are presented separately for each class. The first column shows the dataset and the following class used to find the classification rules. The remaining columns show the fitness results in the format (\(AverageValue\pm ConfidenceValue\)) of the 100 executions for each method. Full results of the UCI datasets can be seen in ([[https://figshare.com/articles/figure/11562996](https://figshare.com/articles/figure/11562996)]([https://figshare.com/articles/figure/11562996](https://figshare.com/articles/figure/11562996)) (accessed on 9 January 2025)) and for the COVID-19 dataset in ([[https://figshare.com/articles/figure/13650476](https://figshare.com/articles/figure/13650476)]([https://figshare.com/articles/figure/13650476](https://figshare.com/articles/figure/13650476)) (accessed on 9 January 2025)). As can be seen, good fitness values were obtained in all three methods. For the breast-w dataset, BIN-NLCEE obtained better results than CEE, with a fitness of 97.89 (ranging from 97.54 to 98.24) for the malignant class and 98.44 (98.17 to 98.73) for the benign class. CEE obtained 96.45 (96.03 to 96.87) for malignant and 97.46 (97.13 to 97.79) for benign. NLCEE obtained a fitness of 97.18 (ranging from 96.81 to 97.55) for the malignant class and 98.09 (97.76 to 98.42) for the benign class, which is statistically equal to BIN-NLCEE. Figure 3: BIN-NLCEE gene—left part of the chromosome. The right part of the chromosome has the same structure as the left side. For the diabetes dataset, BIN-NLCEE obtained better results than NLCEE for both classes. BIN-NLCEE achieved a fitness of 83.34 in the tested_negative class (ranging from 82.69 to 83.99) and 82.34 (81.7 to 82.98) in the tested_positive class. NLCEE achieved a fitness value of 82.00 (81.35 to 82.65) in the tested_negative class and 80.93 (80.27 to 81.59) in the tested_positive class. If compared with CEE results, BIN-NLCEE also obtained better results in both classes, where CEE achieved a fitness of 80.92 (80.26 to 81.58) in the tested_negative class and 80.21 (79.49 to 80.93) in the tested_positive class. For the [PERSON]'s dataset, BIN-NLCEE almost obtained better results for the healthy class than NLCEE, obtaining a fitness of 99.27 (ranging from 98.94 to 99.60) while NLCEE achieved a fitness of 98.53 (98.11 to 98.95). CEE achieved a fitness of 97.92 (97.41 to 98.43), worse than the fitness value of BIN-NLCEE and equal to the fitness value of NLCEE. For the PD class, all three methods obtained the same results according to the confidence value. BIN-NLCEE obtained a fitness of 97.34 (ranging from 96.79 to 97.89) while NLCEE and CEE obtained 95.78 (94.66 to 96.90) and 96.13 (95.41 to 96.85), respectively. For the mammography dataset, all three methods obtained equal results according to the confidence value. BIN-NLCEE achieved a fitness of 85.86 (ranging from 85.21 to 86.51) for the benign class and 84.23 (83.64 to 84.82) for the malignant class. NLCEE and CEE achieved a fitness of 85.64 (85.00 to 86.28) and 85.23 (84.60 to 85.86) for the benign class and 84.28 (83.64 to 84.92) and 83.78 (83.16 to 84.40) for the malignant class, respectively. Finally, for the COVID-19 dataset, BIN-NLCEE achieved better results in the tested_negative class compared to CEE. BIN-NLCEE obtained an average fitness of 64.56 (64.17 to 64.95), NLCEE obtained an average fitness of 64.22 (63.81 to 64.63), and CEE obtained an average fitness of 63.52 (63.13 to 63.91). In the tested_positive class, BIN-NLCEE achieved better