Table 4.

Comparison of classification techniques

Classifier	Toddler		Child		Adolescent		Adult
	F1	Acc. (%)	F1	Acc. (%)	F1	Acc. (%)	F1	Acc. (%)
OneR	0.81	80.551	0.79	78.98	0.76	76.63	0.83	83.18
PART	0.95	94.59	0.90	90.37	0.87	87.10	0.94	94.45
JRip (RIPPER)	0.94	93.45	0.87	87.43	0.85	84.68	0.94	94.28
Ridor	0.92	92.40	0.88	87.82	0.85	85.08	0.93	92.94
Nneg	0.93	92.60	0.82	82.32	0.81	81.05	0.88	88.55
Naive Bayes	0.96	95.54	0.93	92.93	0.91	91.13	0.94	94.10
LibSVM	0.97	96.96	0.49	49.31	0.93	92.74	0.96	96.42
Multilayer Perceptron (MLP)	1.0	100	1.0	100	0.98	98.79	1.0	100
Logistic regression (LR)	0.97	99.62	0.99	99.21	0.95	94.76	0.97	97.32
Simple logistic (SL)	1.0	100	0.99	99.80	0.96	95.97	0.99	99.82
SMO	1.0	100	1.0	100	0.97	97.18	1.0	100
IBK	0.93	92.70	0.88	88.016	0.89	88.71	0.92	92.13
KStar	0.95	94.50	0.84	84.09	0.88	88.31	0.93	92.58
LWL	0.85	84.54	0.79	78.98	0.79	79.03	0.79	78.35
Bagging	0.93	93.17	0.80	80.16	0.76	76.21	0.87	87.30
Iterative classifier optimizer (ICO)	1.0	100	0.99	99.41	0.97	97.18	0.99	99.91
LogitBoost (LB)	1.0	100	0.99	99.41	0.97	97.18	0.99	99.91
Multi class classifier	0.99	99.62	0.99	99.21	0.95	94.76	0.97	97.32
Real Adaboost (RAB)	1.0	100	0.99	99.80	0.97	97.18	0.99	99.82
Hoeffding Tree	0.95	95.25	0.91	91.16	0.90	90.73	0.93	92.84
J48 (C4.5)	0.90	90.32	0.89	88.99	0.81	81.85	0.93	93.29
LMT	1.0	100	0.99	99.80	0.96	95.96	0.99	99.82
NBTree	0.96	95.54	0.93	93.12	0.87	86.69	0.94	94.19
Random Forest	0.95	95.45	0.88	87.63	0.88	87.5	0.92	92.13
Random Tree	0.91	91.08	0.79	78.59	0.79	79.84	0.86	85.87
Simple CART	0.91	90.89	0.81	81.34	0.81	81.05	0.90	89.62
SysFor	0.93	92.79	0.87	86.84	0.86	85.89	0.93	93.38

Acc. accuracy