From: EnsInfer: a simple ensemble approach to network inference outperforms any single method
Ensemble model | Interval between time-series data points | ||||
---|---|---|---|---|---|
10 min | 20 min | 25 min | 50 min | 100 min | |
Logistic regression | 1.11 ± 0.45 | 0.99 ± 0.21 | 0.92 ± 0.34 | 1.33 ± 0.56 | 1.25 ± 0.44 |
Logistic regression\(^+\) | 1.13 ± 0.48 | 1.12 ± 0.43 | 0.95 ± 0.35 | 1.31 ± 0.54 | 1.24 ± 0.46 |
Logistic regression with SGD | 0.25 ± 0.06 | 0.4 ± 0.47 | 0.2 ± 0.11 | 0.2 ± 0.14 | 0.26 ± 0.19 |
Logistic regression with SGD\(^+\) | 0.71 ± 0.23 | 0.63 ± 0.19 | 0.54 ± 0.08 | 0.55 ± 0.15 | 0.57 ± 0.21 |
Naive Bayes | 0.83 ± 0.23 | 0.71 ± 0.17 | 0.65 ± 0.15 | 0.67 ± 0.16 | 0.68 ± 0.26 |
Naive Bayes\(^+\) | 1.33 ± 0.58 | 1.09 ± 0.26 | 1.19 ± 0.5 | 1.14 ± 0.48 | 1.13 ± 0.35 |
Support vector machine | 0.21 ± 0.09 | 0.29 ± 0.14 | 0.26 ± 0.09 | 0.25 ± 0.15 | 0.26 ± 0.22 |
Support vector machine\(^+\) | 0.47 ± 0.2 | 0.51 ± 0.19 | 0.43 ± 0.12 | 0.86 ± 0.6 | 0.47 ± 0.25 |
K-nearest neighbors | 0.59 ± 0.24 | 0.45 ± 0.22 | 0.51 ± 0.2 | 0.39 ± 0.34 | 0.57 ± 0.41 |
K-nearest neighbors\(^+\) | 0.58 ± 0.23 | 0.68 ± 0.54 | 0.71 ± 0.47 | 0.6 ± 0.46 | 0.65 ± 0.31 |
Random forest | 1.12 ± 0.56 | 0.9 ± 0.21 | 0.99 ± 0.29 | 1.73 ± 1.03 | 1.09 ± 0.38 |
Random forest\(^+\) | 1.1 ± 0.51 | 0.97 ± 0.42 | 1.04 ± 0.37 | 1.73 ± 1.1 | 1.08 ± 0.34 |
Adaptive boosting | 1.12 ± 0.5 | 1.13 ± 0.76 | 0.73 ± 0.26 | 1.27 ± 0.65 | 0.95 ± 0.46 |
Adaptive boosting\(^+\) | 1.07 ± 0.43 | 1.11 ± 0.65 | 0.82 ± 0.27 | 1.3 ± 0.7 | 1.03 ± 0.49 |
XGBoost | 0.65 ± 0.34 | 0.56 ± 0.16 | 0.61 ± 0.28 | 1.35 ± 0.86 | 0.66 ± 0.24 |
XGBoost\(^+\) | 0.6 ± 0.25 | 0.58 ± 0.23 | 0.78 ± 0.63 | 1.31 ± 0.86 | 0.67 ± 0.26 |
Best level 1 method in training | 0.88 ± 0.23 | 0.88 ± 0.2 | 0.88 ± 0.24 | 1 ± 0 | 0.81 ± 0.22 |
Best level 1 method evaluated on all samples | 0.93 ± 0.26 | 1.11 ± 0.58 | 1.08 ± 0.34 | 1.03 ± 0.49 | 0.95 ± 0.44 |
Average rank of level 1 methods | 0.95 ± 0.42 | 0.88 ± 0.52 | 0.78 ± 0.39 | 0.69 ± 0.16 | 0.61 ± 0.22 |