Prediction and Classification of Phenol Contents in Cnidium officinale Makino Using a Stacking Ensemble Model in Climate Change Scenarios
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Architecture
2.2. Data Collection
2.3. Data Augmentation
2.4. Assigning Phenol Content Grades Using DPPH Radical Scavenging Activity
2.5. Stacking Ensemble Model for the Prediction and Classification of Phenol Contents in C. officinale Makino
3. Results
3.1. Prediction of Total Phenol Contents in XGB and the Proposed Stacking Ensemble Regression Model
3.2. Classification of Phenol Content Grade in XGB and the Proposed Stacking Ensemble Classification Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Similarity Measurement | SSP1-2.6 | SSP3-7.0 | SSP5-8.5 |
---|---|---|---|
Cosine similarity | 0.990 | 0.990 | 0.990 |
Correlation similarity | 0.996 | 0.996 | 0.998 |
Jensen–Shannon similarity | 0.973 | 0.975 | 0.979 |
IC50 | SSP1-2.6 | SSP3-7.0 | SSP5-8.5 | Legend |
---|---|---|---|---|
IC50min (Chigh) | 0.04 (1) | 0.04 (1) | 0.04 (1) | |
IC50 of maximum phenol content in collected data (phenol content grade) | 0.32 (3) | 0.32 (3) | 0.20 (3) | |
IC50 of minimum phenol content in collected data (phenol content grade) | 0.47 (5) | 0.57 (5) | 0.32 (3) | |
IC50max (Clow) | 1.00 (10) | 1.00 (10) | 1.00 (10) |
Order of Model Selection | 1st | 2nd | 3rd | 4th | 5th |
---|---|---|---|---|---|
Regression base models | XGB | kNN | LinearR | LGBM | ADA |
Classification base models | XGB | DT | ADA | kNN | CAT |
Feature Variable Group | Physiological Response Indicators (O/-) | ||
---|---|---|---|
Chlorophyll Contents (Chl) | Energy Flux per Reaction Center (EF) | Photosynthesis Activity (PA) | |
None | - | - | - |
Chl | O | - | - |
EF | - | O | - |
PA | - | - | O |
Chl-EF | O | O | - |
Chl-PA | O | - | O |
EF-PA | - | O | O |
Chl-EF-PA | O | O | O |
Model | Hyperparameters | ||
---|---|---|---|
Name | Search Range | Optimized Hyperparameter Values | |
kNN | n_neighbors | 3 to 10, step = 1 | 3 |
P | 1.0 to 2.0, step = 1.0 | 2.0 | |
Metric | {‘manhattan’, ‘minkowski’} | ‘minkowski’ | |
DT | criterion | {‘squared_error’, ‘poisson’, | squared_error |
‘friedman_mse’, ‘absolute_error’} | |||
max_depth | 1 to 10, step = 1 | 7 | |
max_leaf_nodes | 2 to 10, step = 1 | 10 | |
min_samples_split | 2 to 10, step = 2 | 2 | |
min_samples_leaf | 1 to 4, step = 1 | 2 | |
RF | n_estimators | 10 to 2000, step = 1 | 953 |
max_features | {‘auto’, ‘sqrt’} | Sqrt | |
max_depth | 1 to 16, step = 1 | 15 | |
min_samples_split | 2 to 10, step = 2 | 2 | |
min_samples_leaf | 1 to 4, step = 1 | 3 | |
XGB | n_estimators | 10 to 2000, step = 1 | 1760 |
max_depth | 1 to 20, step = 1 | 17 | |
learning_rate | 0.01 to 1.00, step = 0.01 | 0.22 | |
alpha | 0.01 to 1.00, step = 0.01 | 0.01 | |
LGBM | n_estimators | 10 to 2000, step = 1 | 1515 |
max_depth | 1 to 20, step = 1 | 20 | |
min_child_weight | 1 to 300, step = 1 | 276 | |
min_child_samples | 10 to 50, step = 1 | 11 | |
learning_rate | 0.01 to 1.00, step = 0.01 | 0.68 | |
ADA | n_estimators | 10 to 2000, step = 1 | 50 |
learning_rate | 0.01 to 1.00, step = 0.01 | 0.1 |
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Lee, H.; Koo, H.J.; Lee, K.C.; Song, Y.; Joo, W.-K.; Chae, C.-J. Prediction and Classification of Phenol Contents in Cnidium officinale Makino Using a Stacking Ensemble Model in Climate Change Scenarios. Agronomy 2024, 14, 1766. https://doi.org/10.3390/agronomy14081766
Lee H, Koo HJ, Lee KC, Song Y, Joo W-K, Chae C-J. Prediction and Classification of Phenol Contents in Cnidium officinale Makino Using a Stacking Ensemble Model in Climate Change Scenarios. Agronomy. 2024; 14(8):1766. https://doi.org/10.3390/agronomy14081766
Chicago/Turabian StyleLee, Hyunjo, Hyun Jung Koo, Kyeong Cheol Lee, Yoojin Song, Won-Kyun Joo, and Cheol-Joo Chae. 2024. "Prediction and Classification of Phenol Contents in Cnidium officinale Makino Using a Stacking Ensemble Model in Climate Change Scenarios" Agronomy 14, no. 8: 1766. https://doi.org/10.3390/agronomy14081766