Journal of Big Data

Table 4 The compared results on the Cracking Dataset

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

Method	RMSE	\(R^2\)	MAE	MAPE
Random forest [5]	0.782	0.401	0.236	0.694
XGBoost [6]	0.771	0.417	0.233	0.687
LightGBM [7]	0.763	0.430	0.247	0.726
CatBoost [8]	0.760	0.433	0.228	0.627
MLP [3]	0.758	0.437	0.233	0.681
ResNet [14]	0.762	0.431	0.243	0.757
FT-transformer [10]	0.759	0.435	0.227	0.626
TabNet [9]	0.766	0.425	0.250	0.650
Ours	0.728	0.481	0.218	0.573

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