Table B2 Evaluation of concreteness prediction by different regression algorithms on the different sets of aggregation strategies
From: Towards hypergraph cognitive networks as feature-rich models of knowledge
Non-Net | G: Ego-Net | G: Louvain | G: EVA | G: Lemon | Hypergraph | |||
|---|---|---|---|---|---|---|---|---|
Linear Regression | RMSE | M | 1.17 | 1.09 | 1.45 | 1.45 | 1.18 | 1.08 |
SE | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 | ||
\(R^{2}\) | M | 0.33 | 0.42 | 0.02 | 0.03 | 0.31 | 0.44 | |
SE | 0.05 | 0.06 | 0.04 | 0.04 | 0.05 | 0.05 | ||
Support Vector Machine | RMSE | M | 0.98 | 0.98 | 1.44 | 1.43 | 1.03 | 0.93 |
SE | 0.03 | 0.04 | 0.04 | 0.04 | 0.03 | 0.03 | ||
\(R^{2}\) | M | 0.53 | 0.53 | 0.06 | 0.05 | 0.48 | 0.58 | |
SE | 0.04 | 0.06 | 0.03 | 0.03 | 0.05 | 0.04 | ||
AdaBoost | RMSE | M | 1.13 | 1.10 | 1.45 | 1.45 | 1.16 | 1.06 |
SE | 0.03 | 0.02 | 0.04 | 0.05 | 0.03 | 0.03 | ||
\(R^{2}\) | M | 0.39 | 0.41 | 0.03 | 0.03 | 0.36 | 0.49 | |
SE | 0.03 | 0.04 | 0.03 | 0.04 | 0.03 | 0.03 |