|
Dataset
|
No. biomes
|
No. samples
|
All features
|
Selected features
|
|---|
|
Pr
|
Rc
|
Acc
|
Fmax
|
AUC
|
Pr
|
Rc
|
Acc
|
Fmax
|
AUC
|
|---|
|
Combined
|
114
|
125,823
|
0.826
|
0.662
|
0.995
|
0.740
|
0.971
|
0.868
|
0.774
|
0.997
|
0.820
|
0.977
|
|
Human
|
25
|
53,553
|
0.822
|
0.521
|
0.984
|
0.695
|
0.972
|
0.894
|
0.826
|
0.991
|
0.863
|
0.984
|
|
Water
|
44
|
27,667
|
0.842
|
0.766
|
0.992
|
0.803
|
0.966
|
0.854
|
0.764
|
0.992
|
0.813
|
0.971
|
|
Soil
|
16
|
11,528
|
0.915
|
0.778
|
0.986
|
0.850
|
0.974
|
0.892
|
0.881
|
0.989
|
0.890
|
0.982
|
|
FEAST
|
3
|
10,270
|
0.793
|
0.795
|
0.984
|
0.803
|
0.980
|
0.895
|
0.812
|
0.989
|
0.862
|
0.991
|
- ONN4MST achieved the accuracy higher than 0.98 for all five datasets, and the AUC higher than 0.97 for all five datasets. For each dataset, we used the model trained on that dataset for evaluation. The evaluation procedure of the ONN model is described in the “Methods” section. ONN4MST based on all features and selected features were both evaluated at the bottom (sixth) layer with a threshold of 0.5
- Abbreviations: Pr precision, Rc recall, Acc accuracy
