Table 6 Performance of classifiers using ensemble stacking methods on a mathematics dataset.

From: Advancing educational data mining for enhanced student performance prediction: a fusion of feature selection algorithms and classification techniques with dynamic feature ensemble evolution

Algorithms

Mathematics dataset

Ensemble staking methods

Chi-square

Information gain

Correlation heat map

Accuracy

CV-score

Accuracy

CV-score

Accuracy

CV-score

DT

90.94

94.11

94.17

89.07

92.02

91.59

RF

95.80

94.38

91.09

92.95

93.46

92.43

SVM

88.76

93.27

89.85

94.11

91.30

90.75

NN

89.49

90.11

89.66

88.16

92.39

90.75

NB

90.57

92.43

88.76

96.63

92.02

91.59

J48

90.59

94.11

90.57

93.11

94.20

91.59

  1. The bold values in the table represent the highest Accuracy and CV-score for each classification method, highlighting their optimal performance across different evaluation metrics.
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