An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning
Table 4
Summary of all extensive experiments to select the best performing preprocessing feature selection methods with the number of features and regression models.
ML method (s)
Preprocessing
Algorithm
n_features
Performance (R2)
SVR
Raw data
N/A
6
0.898
Processed data
N/A
4
0.919
Corr
4
0.931
PCA
4
0.923
ICA
4
0.923
RFR
Raw data
N/A
6
0.918
Processed data
N/A
4
0.929
Corr
4
0.966
PCA
4
0.967
ICA
4
0.952
MLP
Raw data
N/A
6
0.845
Processed data
N/A
4
0.911
Corr
4
0.957
PCA
4
0.961
ICA
4
0.950
ELM
Raw data
N/A
6
0.841
Processed data
N/A
4
0.849
Corr
4
0.942
PCA
4
0.887
ICA
4
0.909
GBR
Raw data
N/A
6
0.934
Processed data
N/A
4
0.944
Corr
4
0.969
PCA
4
0.949
ICA
4
0.934
XGBR
Raw data
N/A
6
0.949
Processed data
N/A
4
0.953
Corr
4
0.974
PCA
4
0.956
ICA
4
0.952
N/A = none. Note: the best approaches were shown in bold type.