Journal of Big Data

Table 5 The compared results on the deflection dataset

From: ASENN: attention-based selective embedding neural networks for road distress prediction

Method	RMSE	\(R^2\)	MAE	MAPE
Random Forest [5]	0.017	0.999	0.005	0.012
XGBoost [6]	0.010	0.999	0.003	0.010
LightGBM [7]	0.021	0.999	0.007	0.017
CatBoost [8]	0.024	0.999	0.007	0.029
MLP [3]	0.110	0.986	0.027	0.147
ResNet [14]	0.038	0.998	0.025	0.213
FT-transformer [10]	0.009	0.999	0.006	0.022
TabNet [9]	0.060	0.995	0.044	0.182
Ours	0.002	0.999	0.001	0.006

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