Table 1.

The Weka classifier models and their accuracy of prediction on the independent test set

Classifier	Accuracy (%)	Classifier	Accuracy (%)	Classifier	Accuracy (%)
NBTree	77.1435	Random forest	71.6437	Voted perceptron	60.9159
SMO	76.9305	Classification via regression	71.1627	IB1	60.7905
Decision table	76.2861	Bayes Net	71.1209	IBk	60.7905
Attribute selected classifier	76.2233	Rotation forest	70.4308	Multilayer perceptron	59.7449
Filtered classifier	76.1188	LADTree	70.2217	Naïve bayes multinomial	58.9921
Bagging	75.366	LogitBoost	69.6989	Complement naïve bayse	58.9293
Decorate	75.0314	ADTree	68.4442	Naïve bayes simple	58.7829
JRip	74.4458	FT	68.2978	Naïve bayes	58.5529
END	74.3622	AdaBoostM1	67.2313	Naïve bayes multinom updateable	58.5529
Nested Dichotomies Class Balanced ND	74.3622	RandomTree	66.1857	Naïve bayes updateable	58.5529
Nested Dichotoies Data Near Balanced ND	74.3622	Raced incremental logitBoost	65.0774	DMNBtext	57.7373
Nested Dichotomies ND	74.3622	OneR	63.279	Threshold selector	56.3363
Ordinal class classifier	74.3622	Conjunctive rule	62.4843	RBF network	56.0435
J48	74.3622	KStar	61.9824	VFI	51.7984
PART	74.3413	LWL	61.857	Classification via clustering	50.2928
J48graft	74.3413	Decision stump	61.857	CV parameter selection	49.9791
Random sub space	74.2158	SPegasos	61.7942	Grading	49.9791
Simple cart	73.923	Multi boost AB	61.7315	Multi scheme	49.9791
LMT	73.6303	NNge	61.7315	Stacking	49.9791
Ridor	73.4421	Bayesian logistic regression	61.606	StackingC	49.9791
BFTree	73.3166	Logistic	61.5851	Vote	49.9791
DTNB	73.1493	Multi class classifier	61.5851	ZeroR	49.9791
REPTree	72.3756	Simple logistic	61.376	Hyper pipes	49.9164
Random committee	71.9155	Dagging	61.2505