Skip to main content

Table 15 Computational time (seconds) of the boosting algorithms

From: Boosting methods for multi-class imbalanced data classification: an experimental review

Dataset Algorithm
AdaBoost.MH SAMME CatBoost LogitBoost GradientBoost XGBoost MEBoost SMOTEBoost RUSBoost LightGBM AdaC1 AdaC2 AdaC3 AdaCost
Wine 0.01 0.01 0.79 0.85 0.35 0.03 1.37 0.02 0.72 0.06 0.02 0.01 0.01 0.01
Hayes-Roth 0.01 0.34 0.17 0.81 0.66 0.02 1.45 1.07 0.50 0.06 0.01 0.02 0.01 0.01
Contraceptive 1.11 0.57 0.39 4.53 1.81 0.08 7.41 16.11 1.17 0.16 0.45 0.57 0.57 0.71
Pen-based 0.27 1.04 2.65 7.19 3.62 0.20 27.90 1.26 1.74 1.40 0.32 0.36 0.32 0.41
Vertebral column 0.02 0.54 0.65 1.21 0.54 0.03 3.08 5.45 0.72 0.13 0.02 0.02 0.01 0.02
New thyroid 0.01 0.01 0.48 0.87 0.38 0.02 0.56 4.91 0.52 0.05 0.01 0.01 0.02 0.01
Dermatology 0.08 0.71 0.47 2.63 1.02 0.07 2.42 7.20 1.18 0.51 0.05 0.06 0.33 0.08
Balance scale 0.75 0.40 0.63 1.13 1.22 0.04 5.59 14.67 0.89 0.11 0.62 0.55 0.46 0.55
Glass 0.05 0.36 0.71 1.65 0.87 0.05 2.95 3.46 0.65 0.11 0.05 0.06 0.06 0.06
Heart 0.17 0.44 0.44 1.74 1.00 0.53 14.87 9.10 0.69 0.09 0.17 0.30 1.20 0.13
Car evaluation 0.41 0.70 0.45 1.91 1.85 0.08 5.93 89.18 0.99 0.13 0.07 0.11 0.80 0.08
Thyroid 9.63 2.69 2.48 6.08 2.18 0.35 1.86 Hours 1.44 0.48 1.35 2.58 0.93 2.91
Yeast 1.26 0.79 1.22 6.35 4.79 0.32 24.84 20.33 0.91 0.90 0.27 0.23 0.32 0.41
Page blocks 2.89 3.94 2.75 12.19 4.05 0.40 14.66 116.07 1.22 0.31 1.57 1.63 1.66 8.49
Shuttle 0.14 9.40 8.64 58.01 74.24 3.31 331.38 Hours 10.02 0.15 0.06 0.06 0.06 0.06
Average 1.12 1.46 1.53 7.14 6.57 0.37 29.75 Hours 1.56 0.31 0.33 0.44 0.45 0.93
  1. The best comptional time is shown in italic for each dataset