Scenario 2 - Epileptic Seizure Classification ================================================ Configuration loaded: 3 folds, 2 feature options, 2 radius options. Loading dataset from ./Datasets/epileptic_seizure_data.csv ... Dataset loaded: 11500 samples, 178 features, 5 classes. Splitting data into train/val/test (60/20/20%)... -> train: 6900 val: 2300 test: 2300 Applying z-score normalization... GRID SEARCH (features × radius) using 3-fold CV [GRID] features= 5, radius=0.50 ... -> Fold 1/3 ... ANFIS info: Number of nodes: 92 Number of linear parameters: 7 Number of nonlinear parameters: 70 Total number of parameters: 77 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 7 Minimal training RMSE = 1.13032 Minimal checking RMSE = 1.13236 Overall Accuracy: 30.61% Cohen's Kappa : 0.169 kappa=0.169 rules=7 -> Fold 2/3 ... ANFIS info: Number of nodes: 140 Number of linear parameters: 11 Number of nonlinear parameters: 110 Total number of parameters: 121 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 11 Minimal training RMSE = 1.12499 Minimal checking RMSE = 1.15105 Overall Accuracy: 39.43% Cohen's Kappa : 0.289 kappa=0.289 rules=11 -> Fold 3/3 ... ANFIS info: Number of nodes: 128 Number of linear parameters: 10 Number of nonlinear parameters: 100 Total number of parameters: 110 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 10 Minimal training RMSE = 1.13166 Minimal checking RMSE = 1.12551 Overall Accuracy: 34.91% Cohen's Kappa : 0.225 kappa=0.225 rules=10 -> mean Kappa=0.227 mean rules=9 [GRID] features= 5, radius=0.75 ... -> Fold 1/3 ... ANFIS info: Number of nodes: 68 Number of linear parameters: 5 Number of nonlinear parameters: 50 Total number of parameters: 55 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 5 Minimal training RMSE = 1.12389 Minimal checking RMSE = 1.12597 Overall Accuracy: 33.17% Cohen's Kappa : 0.210 kappa=0.210 rules=5 -> Fold 2/3 ... ANFIS info: Number of nodes: 68 Number of linear parameters: 5 Number of nonlinear parameters: 50 Total number of parameters: 55 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 5 Minimal training RMSE = 1.15261 Minimal checking RMSE = 1.16568 Overall Accuracy: 34.30% Cohen's Kappa : 0.240 kappa=0.240 rules=5 -> Fold 3/3 ... ANFIS info: Number of nodes: 68 Number of linear parameters: 5 Number of nonlinear parameters: 50 Total number of parameters: 55 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 5 Minimal training RMSE = 1.14349 Minimal checking RMSE = 1.13975 Overall Accuracy: 34.65% Cohen's Kappa : 0.226 kappa=0.226 rules=5 -> mean Kappa=0.225 mean rules=5 [GRID] features= 8, radius=0.50 ... -> Fold 1/3 ... ANFIS info: Number of nodes: 209 Number of linear parameters: 11 Number of nonlinear parameters: 176 Total number of parameters: 187 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 11 Minimal training RMSE = 1.11568 Minimal checking RMSE = 1.12592 Overall Accuracy: 35.26% Cohen's Kappa : 0.224 kappa=0.224 rules=11 -> Fold 2/3 ... ANFIS info: Number of nodes: 209 Number of linear parameters: 11 Number of nonlinear parameters: 176 Total number of parameters: 187 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 11 Minimal training RMSE = 1.08705 Minimal checking RMSE = 1.11471 Overall Accuracy: 35.48% Cohen's Kappa : 0.226 kappa=0.226 rules=11 -> Fold 3/3 ... ANFIS info: Number of nodes: 209 Number of linear parameters: 11 Number of nonlinear parameters: 176 Total number of parameters: 187 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 11 Minimal training RMSE = 1.12397 Minimal checking RMSE = 1.12198 Overall Accuracy: 35.65% Cohen's Kappa : 0.230 kappa=0.230 rules=11 -> mean Kappa=0.227 mean rules=11 [GRID] features= 8, radius=0.75 ... -> Fold 1/3 ... ANFIS info: Number of nodes: 119 Number of linear parameters: 6 Number of nonlinear parameters: 96 Total number of parameters: 102 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 6 Minimal training RMSE = 1.12245 Minimal checking RMSE = 1.12803 Overall Accuracy: 32.70% Cohen's Kappa : 0.196 kappa=0.196 rules=6 -> Fold 2/3 ... ANFIS info: Number of nodes: 173 Number of linear parameters: 9 Number of nonlinear parameters: 144 Total number of parameters: 153 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 9 Minimal training RMSE = 1.10065 Minimal checking RMSE = 1.12512 Overall Accuracy: 37.09% Cohen's Kappa : 0.251 kappa=0.251 rules=9 -> Fold 3/3 ... ANFIS info: Number of nodes: 101 Number of linear parameters: 5 Number of nonlinear parameters: 80 Total number of parameters: 85 Number of training data pairs: 4600 Number of checking data pairs: 2300 Number of fuzzy rules: 5 Minimal training RMSE = 1.13562 Minimal checking RMSE = 1.13557 Overall Accuracy: 33.00% Cohen's Kappa : 0.196 kappa=0.196 rules=5 -> mean Kappa=0.214 mean rules=7 BEST HYPERPARAMS features=5 radius=0.50 CV Kappa=0.227 mean rules=9 Training final model on train+val with best params ... ANFIS info: Number of nodes: 80 Number of linear parameters: 6 Number of nonlinear parameters: 60 Total number of parameters: 66 Number of training data pairs: 9200 Number of checking data pairs: 2300 Number of fuzzy rules: 6 Minimal training RMSE = 1.14268 Minimal checking RMSE = 1.1463 Final training complete: 6 rules. Evaluating on TEST set ... Overall Accuracy: 29.78% Cohen's Kappa : 0.158 [TEST RESULTS] OA = 29.78 % Kappa= 0.158 Rules= 6 Generating figures ... Done. Figures saved in: figures_scn2