Table 6.

Results achieved on the training set for the vowel /e/

Algorithm	Sensibility (%)	Specificity (%)	Accuracy (%)	Precision (%)	F1-score (%)	AUC
Naive Bayes [25]	66.67	84.85	75.76	81.48	73.33	0.861
Bayes Net [25]	60.61	91.30	76.30	86.96	71.43	0.880
SVM [6]	96.97	95.45	96.21	95.52	96.24	0.960
SGD [3]	100.00	100.00	100.00	100.00	100.00	1.000
Ibk [2]	100.00	100.00	100.00	100.00	100.00	1.000
LWL [14]	78.79	98.48	88.64	98.11	87.39	0.941
Adaboost [12]	80.30	96.97	88.64	96.36	87.60	0.965
Bagging [12]	89.39	87.88	88.64	88.06	88.72	0.961
OneR [19]	86.36	71.21	78.79	75.00	80.28	0.788
Decision Table [26]	89.16	80.30	86.64	91.93	90.52	0.800
J48 [41]	96.97	98.48	97.73	98.46	97.71	0.991
Random Forest [49]	100.00	100.00	100.00	100.00	100.00	1.000