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Ensembling Methods for Selecting a Splitting Attribute in Decision Trees Learning Algorithms

Table 2: Effectiveness of attribute selection methods

Methods for selecting a splitting attribute Classification accuracy on the task:
1 2 3 4 5 6 7 8 9 10 11 12
Correlation-based 0.56 0.41 0.28 0.84 0.96 0.75 0.93 0.2 0.69 0.57 0.72 0.37
Chi-Squared Score 0.51 0.39 0.33 0.58 0.76 0.7 0.81 0.46 0.76 0.68 0.79 0.25
Variance ratio 0.45 0.42 0.41 0.88 0.95 0.64 0.88 0.73 0.70 0.68 0.8 0.31
Fast correlation-based filter 0.41 0.33 0.16 0.86 0.65 0.71 0.88 0.2 0.73 0.54 0.75 0.21
Fisher score 0.54 0.38 0.27 0.87 0.76 0.75 0.89 0.35 0.74 0.75 0.8 0.40
Mean absolute difference 0.49 0.53 0.24 0.88 0.92 0.67 0.95 0.82 0.59 0.82 0.74 0.23
Variance 0.49 0.55 0.24 0.88 0.92 0.68 0.95 0.81 0.59 0.82 0.74 0.23
ReliefF 0.63 0.56 0.24 0.85 0.95 0.65 0.96 0.86 0.69 0.83 0.8 0.24
Separation Measure 0.69 0.55 0.75 0.88 0.95 0.73 0.9 0.93 0.77 0.83 0.79 0.43
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