EMT: Ensemble Meta-Based Tree Model for Predicting Student Performance

Table 4

Comparison between five groups of classification algorithms (accuracy and F-measure values).

Algorithm’s family	Algorithm name	Dataset
		Accuracy	F-measure
Rules	ConjunctiveRule	0.633	0.533
	DecisionTable	0.850	0.849
	DTNB	0.855	0.854
	FURIA	0.888	0.887
	JRip	0.873	0.872
	LAC	0.780	0.781
	MODLEM	0.873	0.872
	MultiobjectiveEvolutionaryFuzzy	0.628	0.624
	NNge	0.830	0.831
	OLM	0.668	0.661
	OneR	0.695	0.692
	PART	0.918	0.918
	Ridor	0.875	0.874
	RoughSet	0.870	0.869
	ZeroR	0.433	0.261
Bayes	A1DE	0.880	0.880
	A2DE	0.895	0.895
	BayesNet	0.850	0.849
	NaiveBayes	0.773	0.769
Function	LibLINEAR	0.690	0.688
	LibSVM	0.540	0.470
	MLPClassifier	0.893	0.892
	Multilayer perceptron	0.910	0.910
	MultilayerPerceptronCS	0.910	0.910
	RBFClassifier	0.853	0.853
	SMO	0.870	0.870
Lazy	IB1	0.838	0.837
	IBK	0.838	0.837
	IBKLG	0.838	0.837
	KStar	0.863	0.862
	LocalKnn	0.928	0.927
	RseslibKnn	0.928	0.927
Trees	BFTree	0.905	0.905
	CDT	0.903	0.902
	DecisionStump	0.558	0.406
	ForestPA	0.910	0.910
	FT	0.883	0.883
	HoeffdingTree	0.773	0.769
	J48	0.943	0.943
	J48Consolidated	0.940	0.940
	LADTree	0.900	0.900
	LMT	0.933	0.932
	NBTree	0.925	0.925
	RandomForest	0.935	0.935
	RandomTree	0.828	0.827
	REPTree	0.893	0.893
	SimpleCart	0.893	0.892