Dataset

Training +

Validation

Examples

Accuracy (%)

G-measure

NB

ID3

MLP

ECL

NB

ID3

MLP

ECL

Avg ± sd

Max

Avg ± sd

Max

Bankruptcy

124 + 126

98.4

99.2

98.4

98.9 ± 0.770

100.0

0.983

0.993

0.983

0.990 ± 0.007

1.000

Breast

222 + 50

69.1

52.7

65.5

58.0 ± 5.243

69.1

0.555

0.446

0.599

0.537 ± 0.063

0.658

Car

1382 + 346

93.4

94.8

98.8

95.5 ± 1.367

97.4

0.927

0.987

0.991

0.967 ± 0.011

0.981

Chess

2557 + 639

89.7

99.5

99.1

96.9 ± 0.896

98.0

0.895

0.996

0.991

0.968 ± 0.009

0.980

Flare

1066 + 1066

92.2

97.9

97.8

90.0 ± 1.542

92.3

0.779

0.817

0.803

0.924 ± 0.009

0.936

Heart

80 + 187

64.7

65.2

65.2

64.9 ± 3.001

71.1

0.650

0.645

0.643

0.635 ± 0.038

0.709

Housevotes

186 + 46

95.7

89.1

93.5

94.2 ± 2.405

97.8

0.960

0.894

0.938

0.942 ± 0.024

0.980

Kr-vs-k

1450 + 1451

57.7

87.0

79.6

87.7 ± 9.492

97.1

0.722

0.752

0.888

0.924 ± 0.056

0.985

Lenses

19 + 5

60.0

60.0

80.0

78.0 ± 6.000

80.0

0.577

0.577

0.816

0.792 ± 0.072

0.816

Mushroom

4515 + 1129

97.3

100.0

100.0

100.0 ± 0.000

100.0

0.967

1.000

1.000

1.000 ± 0.000

1.000

Nursery

10,368 + 2592

100.0

100.0

100.0

100.0 ± 0.000

100.0

1.000

1.000

1.000

1.000 ± 0.000

1.000

Post-operative

70 + 17

76.5

52.9

58.8

55.9 ± 10.097

70.6

0.447

0.381

0.387

0.402 ± 0.185

0.632

Tic-tac-toe

766 + 192

72.4

86.5

97.4

91.2 ± 3.159

98.4

0.592

0.878

0.966

0.874 ± 0.046

0.977

Zoo

81 + 20

90.0

100.0

100.0

99.0 ± 2.550

100.0

0.943

1.000

1.000

0.982 ± 0.068

1.000