An Efficient Task Implementation Modeling Framework with Multi-Stage Feature Selection and AutoML: A Case Study in Forest Fire Risk Prediction
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Source and Processing
Data Type | Data Name | Sources | Spatial Resolution | Temporal Resolution | Time Range | Usage |
---|---|---|---|---|---|---|
Fire data | VIIRS | NASA FIRMS | 375 m | Daily | 2015–2019 | Sample set construction |
Remote sensing product | DEM | NASA SRTM | 30 m | - | 2000 | Terrain feature extraction |
CLCD | [44] | 30 m | Yearly | 2015–2019 | Sample data cleaning | |
CHIRPS | UCSB/CHC | 5566 m | Daily | 2015–2019 | Sample set and feature set construction | |
MYD11A1 | USGS | 1000 m | Daily | 2015–2019 | LST feature extraction | |
Satellite image | MYD09GA | USGS | 500 m | Daily | 2015–2019 | Vegetation feature extraction |
3. Methods
3.1. High-Dimensional Feature Space Construction
3.2. Feature Selection
3.2.1. Traditional Feature Selection Algorithms
3.2.2. Advance Hybrid Feature Selection Algorithms
3.2.3. Multi-Stage Feature Selection Algorithm: MSFS
Algorithm 1 MSFS Algorithm |
Input: feature matrix, target variable, number of features to select using mutual information, estimator for RFECV, number of cross-validation folds, number of repetitions for increased stability Output: final selected feature set, indices of the final selected features 1: Initialize selected_feature_indices_all_runs as an empty list 2: For each repetition from 1 to n_repeats: 3: X_mi, selected_feature_indices_mi ← MIG (X, y, k_mi) 4: If the number of features in X_mi is greater than 1: 5: X_rfecv, selected_feature_indices_rfecv ← RFECV (X_mi, y, estimator, cv) 6: selected_feature_indices ← selected_feature_indices_mi[selected_feature_indices_rfecv] 7: Else: 8: selected_feature_indices ← selected_feature_indices_mi 9: X_rfecv ← X_mi 10: Add selected_feature_indices to the selected_feature_indices_all_runs list 11: Initialize feature_votes as a zero array of length equal to the number of features in X 12: For each feature_indices in selected_feature_indices_all_runs: 13: Increment the votes in feature_votes at the indices corresponding to feature_indices 14: Initialize selected_feature_indices_final as an empty list 15: For each index i in feature_votes: 16: If feature_votes[i] is greater than half of n_repeats: 17: Add i to the selected_feature_indices_final list 18: X_final ← The columns of X corresponding to the indices in selected_feature_indices_final 19: Return X_final and selected_feature_indices_final |
- MIG Filtering
- ➀
- For , calculating the MIG between and target variable , and selecting a subset of features strongly related to is performed, as shown in Equation (1).
- ➁
- Return the feature matrix filtered by MIG and the indices of the selected features.
- 2.
- RFECV Selection
- ➀
- The initial feature set is obtained through MIG filtering.
- ➁
- Modeling is performed using , and then the importance of each feature is calculated.
- ➂
- Recursively remove the least important feature and update the feature set.
- ➃
- Go back to step ② until the importance of all features has been rated.
- ➄
- Based on the feature importance determined during the RFE phase, select different numbers of features sequentially.
- ➅
- Perform cross-validation on the selected feature sets.
- ➆
- Determine the number of features with the highest average score to complete the feature selection.
- ➇
- Return the feature matrix selected by RFECV and the indices of the selected features.
- 3.
- Vote Aggregation Mechanism
- ➀
- Initialize an array of zeros with the same length as the original feature count.
- ➁
- Iterate through the indices of the selected features in each run of and cast votes (i.e., increment the count) for the corresponding positions in .
- ➂
- Select features based on the voting results: choose features that are selected in more than half of the runs (i.e., values greater than n_repeats/2).
- ➃
- Return the aggregated feature matrix and the indices of the selected features.
3.3. Forest Fire Risk Modeling
3.3.1. AutoSklearn Algorithm
- Meta-learning
- 2.
- Bayesian Optimizer
- 3.
- Automated ensemble of models
3.3.2. AutoSklearn Modeling and Parameter Configuration
3.4. Model Performance Assessment
3.4.1. Evaluation Metrics
3.4.2. Environment Setting
4. Results
4.1. Feature Subsets Based on Six FS Algorithms
4.1.1. Number and Contributions of Six Feature Subsets
4.1.2. Capability of FS Algorithms on Handling Collinear Features
4.2. Model Prediction Performance Based on AutoSklearn and FS Algorithms
4.2.1. AutoSklearn Training Process Based on Different Feature Sets
4.2.2. Prediction Performance of Different Feature Sets Based on AutoSklearn
4.2.3. Stability Analysis of FS Algorithms for AutoSklearn Modeling
5. Discussion
5.1. The Reproducible Challenge of AutoSklearn and Advantages of the MSFS Algorithm
5.2. The Value of MSFS–AutoSklearn in the Application of Efficient Task Implementation
5.3. MSFS–AutoSklearn Holds Broad Prospects in Efficient Multi-Task Applications
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Huber, M.; Zller, M. Benchmark and Survey of Automated Machine Learning Frameworks. Artif. Intell. Res. 2021, 70, 409–472. [Google Scholar] [CrossRef]
- Guyon, I.; Chaabane, I.; Escalante, H.J.; Escalera, S.; Jajetic, D.; Lloyd, J.R.; Macià, N.; Ray, B.; Romaszko, L.; Sebag, M.; et al. A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. In Proceedings of the Workshop on Automatic Machine Learning; PMLR: New York, NY, USA, 2016; pp. 21–30. [Google Scholar]
- Alsharef, A.; Aggarwal, K.; Sonia; Kumar, M.; Mishra, A. Review of ML and AutoML Solutions to Forecast Time-Series Data. Arch. Comput. Methods Eng. 2022, 29, 5297–5311. [Google Scholar] [CrossRef] [PubMed]
- Shen, Z.; Zhang, Y.; Wei, L.; Zhao, H.; Yao, Q. Automated Machine Learning: From Principles to Practices. arXiv 2018. [Google Scholar] [CrossRef]
- Wever, M.; Tornede, A.; Mohr, F.; Hullermeier, E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Trans. Pattern Anal. 2021, 43, 3037–3054. [Google Scholar] [CrossRef]
- Karmaker Santu, S.K.; Hassan, M.M.; Smith, M.J.; Xu, L.; Zhai, C.; Veeramachaneni, K. AutoML to Date and Beyond: Challenges and Opportunities. ACM Comput. Surv. 2022, 54, 1–36. [Google Scholar] [CrossRef]
- Chollet, F. Xception: Deep Learning with Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 1251–1258. [Google Scholar]
- Hu, J.; Shen, L.; Albanie, S.; Sun, G.; Wu, E. Squeeze-and-Excitation Networks. IEEE Trans. Pattern Anal. Mach. Intell. 2020, 42, 2011–2023. [Google Scholar] [CrossRef] [PubMed]
- Feurer, M.; Klein, A.; Eggensperger, K.; Springenberg, J.; Blum, M.; Hutter, F. Efficient and Robust Automated Machine Learning. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 7–12 December 2015; pp. 2755–2763. [Google Scholar]
- Li, J.; Cheng, K.; Wang, S.; Morstatter, F.; Trevino, R.P.; Tang, J.; Liu, H. Feature Selection. ACM Comput. Surv. 2018, 50, 1–45. [Google Scholar] [CrossRef]
- Kumar, V.; Minz, S. Feature Selection: A literature Review. Smart Comput. Rev. 2014, 4, 211–229. [Google Scholar] [CrossRef]
- Jovic, A.; Brkic, K.; Bogunovic, N. A review of feature selection methods with applications. In Proceedings of the 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 25–29 May 2015; pp. 1200–1205. [Google Scholar]
- Dhal, P.; Azad, C. A comprehensive survey on feature selection in the various fields of machine learning. Appl. Intell. 2022, 52, 4543–4581. [Google Scholar] [CrossRef]
- Hancer, E.; Xue, B.; Zhang, M. Differential evolution for filter feature selection based on information theory and feature ranking. Knowl.-Based Syst. 2018, 140, 103–119. [Google Scholar] [CrossRef]
- Ruiz, R.; Riquelme, J.C.; Aguilar-Ruiz, J.S. Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn. 2006, 39, 2383–2392. [Google Scholar] [CrossRef]
- Chen, Y.; Bi, J.; Wang, J.Z. MILES: Multiple-instance learning via embedded instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 1931–1947. [Google Scholar] [CrossRef]
- Aram, K.Y.; Lam, S.S.; Khasawneh, M.T. Linear Cost-sensitive Max-margin Embedded Feature Selection for SVM. Expert Syst. Appl. 2022, 197, 116683. [Google Scholar] [CrossRef]
- Bommert, A.; Welchowski, T.; Schmid, M.; Rahnenführer, J. Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Brief. Bioinform. 2022, 23, bbab354. [Google Scholar] [CrossRef] [PubMed]
- Haq, A.U.; Zeb, A.; Lei, Z.; Zhang, D. Forecasting daily stock trend using multi-filter feature selection and deep learning. Expert Syst. Appl. 2021, 168, 114444. [Google Scholar] [CrossRef]
- Vergara, J.R.; Estévez, P.A. A review of feature selection methods based on mutual information. Neural Comput. Appl. 2014, 24, 175–186. [Google Scholar] [CrossRef]
- Tan, M.; Pu, J.; Zheng, B. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. Int. J. Comput. Ass. Rad. 2014, 9, 1005–1020. [Google Scholar] [CrossRef]
- Liu, Y.; Zheng, Y.F. FS_SFS: A novel feature selection method for support vector machines. Pattern Recogn. 2006, 39, 1333–1345. [Google Scholar] [CrossRef]
- Monirul Kabir, M.; Monirul Islam, M.; Murase, K. A new wrapper feature selection approach using neural network. Neurocomputing 2010, 73, 3273–3283. [Google Scholar] [CrossRef]
- Maldonado, S.; Weber, R. A wrapper method for feature selection using Support Vector Machines. Inform. Sci. 2009, 179, 2208–2217. [Google Scholar] [CrossRef]
- Fonti, V.; Belitser, E. Feature Selection using LASSO. In Research Paper in Business Analytics; Vrije Universiteit Amsterdam: Amsterdam, The Netherlands, 2017; pp. 1–25. [Google Scholar]
- Liu, H.; Zhou, M.C.; Liu, Q. An Embedded Feature Selection Method for Imbalanced Data Classification. IEEE/CAA J. Autom. Sin. 2019, 6, 703–715. [Google Scholar] [CrossRef]
- Yin, Y.; Jang-Jaccard, J.; Xu, W.; Singh, A.; Zhu, J.; Sabrina, F.; Kwak, J. IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset. J. Big Data 2023, 10, 15. [Google Scholar] [CrossRef]
- Omuya, E.O.; Okeyo, G.O.; Kimwele, M.W. Feature Selection for Classification using Principal Component Analysis and Information Gain. Expert Syst. Appl. 2021, 174, 114765. [Google Scholar] [CrossRef]
- Bolón-Canedo, V.; Alonso-Betanzos, A. Ensembles for feature selection: A review and future trends. Inform. Fusion 2019, 52, 1–12. [Google Scholar] [CrossRef]
- Khaire, U.M.; Dhanalakshmi, R. Stability of feature selection algorithm: A review. J. King Saud Univ.—Comput. Inf. Sci. 2022, 34, 1060–1073. [Google Scholar] [CrossRef]
- Tu, T.; Su, Y.; Ren, S. FC-MIDTR-WCCA: A Machine Learning Framework for PM2.5 Prediction. IAENG Int. J. Comput. Sci. 2024, 51, 544–552. [Google Scholar]
- Tu, T.; Su, Y.; Tang, Y.; Tan, W.; Ren, S. A More Flexible and Robust Feature Selection Algorithm. IEEE Access 2023, 11, 141512–141522. [Google Scholar] [CrossRef]
- Machado-Silva, F.; Libonati, R.; Lima, T.F.M.D.; De, M.; Dacamara, C.C. Drought and fires influence the respiratory diseases hospitalizations in the Amazon. Ecol. Indic. 2019, 109, 105817. [Google Scholar] [CrossRef]
- Machado, A.; Serpa, D.; Santos, A.K.; Gomes, A.P.; Keizer, J.J.; Oliveira, B.R.F. Effects of different amendments on the quality of burnt eucalypt forest soils—A strategy for ecosystem rehabilitation. J. Environ. Manag. 2022, 320, 115766. [Google Scholar] [CrossRef]
- Vasilakos, C.; Kalabokidis, K.; Hatzopoulos, J.; Matsinos, I. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Nat. Hazards 2009, 50, 125–143. [Google Scholar] [CrossRef]
- Pereira, S.O.A.F. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. For. Ecol. Manag. 2012, 275, 117–129. [Google Scholar] [CrossRef]
- Pourtaghi, Z.S.; Pourghasemi, H.R.; Aretano, R.; Semeraro, T. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol. Indic. 2016, 64, 72–84. [Google Scholar] [CrossRef]
- Moelders, N. Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska. Weather Forecast 2008, 23, 953–973. [Google Scholar] [CrossRef]
- Kumar, M.; Kosović, B.; Nayak, H.P.; Porter, W.C.; Randerson, J.T.; Banerjee, T. Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event. Front. Earth Sci. 2024, 1, 1305124. [Google Scholar] [CrossRef]
- Wei, S.; Li, X.; Wang, Z.; Wu, Z.; Luo, S.; Zhou, Y.; Zhong, Y.; Li, Q. Situation and countermeasures of forest fire prevention in Guangdong Province. Mod. Agric. 2021, 10, 88–90. (In Chinese) [Google Scholar]
- NBSC. China Statistical Yearbook, 2023; China Statistics Press: Beijing, China, 2023. [Google Scholar]
- Zhao, L.; Ge, Y.; Guo, S.; Li, H.; Li, X.; Sun, L.; Chen, J. Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach. For. Ecol. Manag. 2024, 558, 121771. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.; Gao, X.; Ferreira, L. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Yang, J.; Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
- Estevez, P.A.; Tesmer, M.; Perez, C.A.; Zurada, J.M. Normalized Mutual Information Feature Selection. IEEE Trans. Neural Netw. 2009, 20, 189–201. [Google Scholar] [CrossRef]
- Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27, 1226–1238. [Google Scholar] [CrossRef]
- Ververidis, D.; Kotropoulos, C. Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition. Signal Process. 2008, 88, 2956–2970. [Google Scholar] [CrossRef]
- Roth, V. The generalized LASSO. IEEE Trans. Neural Netw. 2004, 15, 16–28. [Google Scholar] [CrossRef] [PubMed]
- Mustaqim, A.Z.; Adi, S.; Pristyanto, Y.; Astuti, Y. The Effect of Recursive Feature Elimination with Cross-Validation (RFECV) Feature Selection Algorithm toward Classifier Performance on Credit Card Fraud Detection. In Proceedings of the International Conference on Artificial Intelligence and Computer Science Technology, Yogyakarta, Indonesia, 29–30 June 2021; pp. 270–275. [Google Scholar]
- Brazdil, P.; Carrier, C.G.; Soares, C.; Vilalta, R. Metalearning: Applications to Data Mining; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
- Turner, R.; Eriksson, D.; McCourt, M.; Kiili, J.; Laaksonen, E.; Xu, Z.; Guyon, I. Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. In NeurIPS 2020 Competition and Demonstration Track; PMLR: New York, NY, USA, 2021; pp. 3–26. [Google Scholar]
- Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R.P.; De Freitas, N. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 2015, 104, 148–175. [Google Scholar] [CrossRef]
- Feurer, M.; Springenberg, T.; Hutter, F. Initializing Bayesian hyperparameter optimization via meta-learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Austin, TX, USA, 25–30 January 2015. [Google Scholar]
- Reif, M.; Shafait, F.; Dengel, A. Meta-learning for evolutionary parameter optimization of classifiers. Mach. Learn. 2012, 87, 357–380. [Google Scholar] [CrossRef]
- Gomes, T.A.F.; Prudencio, R.B.C.; Soares, C.; Rossi, A.L.D.; Carvalho, A. Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 2012, 75, 3–13. [Google Scholar] [CrossRef]
- Wolpert, D.H. Stacked generalization. Neural Netw. 1992, 5, 241–259. [Google Scholar] [CrossRef]
- Caruana, R.A.; Niculescu-Mizil, A.; Crew, G.; Ksikes, A. Ensemble selection from libraries of models. In Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada, 4–8 July 2004. [Google Scholar]
- He, X.; Zhao, K.; Chu, X. AutoML: A Survey of the State-of-the-Art. Knowl. Base. Syst. 2021, 212, 106622. [Google Scholar] [CrossRef]
- Pluim, J.P.W.; Maintz, J.B.A.; Viergever, M.A. Image registration by maximization of combined mutual information and gradient information. In Proceedings of the International Conference on Medical Image Computing & Computer-assisted Intervention, Pittsburgh, PA, USA, 11–14 October 2000; pp. 809–814. [Google Scholar]
- Thevenaz, P.; Unser, M. Optimization of mutual information for multiresolution image registration. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 2000, 9, 2083–2099. [Google Scholar]
Environmental Factors | Indicators | Features |
---|---|---|
Terrain (static) | 1. Elevation | (1) Elevation |
2. Slope | (2) Slope | |
3. Aspect | (3) Aspect | |
Vegetation (dynamic) | 4. NDWI | (4) NDWImax; (5) NDWImean; (6) NDWImedian; (7) NDWImin |
5. NDVI | (8) NDVImax; (9) NDVImean; (10) NDVImedian; (11) NDVImin | |
6. EVI | (12) EVImax; (13) EVImean; (14) EVImedian; (15) EVImin | |
Meteorology (dynamic) | 7. Precipitation | (16) PRECmax; (17) PRECmean; (18) PRECmedian; (19) PRECmin; (20) PRECsum |
8. LST | (21) LSTmax; (22) LSTmean; (23) LSTmedian; (24) LSTmin |
Parameters | Value |
---|---|
time_left_for_this_task | 600 |
per_run_time_limit | 30 |
metric | autosklearn.metrics.accuracy |
seed | 42 |
resampling_strategy | ‘cv’ |
resampling_strategy_arguments | {‘folds’: 5} |
Algorithm | Feature Number of Subset | Number of Collinear Feature Groups | Specific Remaining Features of Each Indicator |
---|---|---|---|
MI | 16 | 8 | EVImax, EVImean, EVImedian, EVImin NDVImax, NDVImean, NDVImedian, NDVImin NDWImean, NDWImedian, NDWImax, NDWImin |
SFS | 15 | 3 | EVImax, EVImedian NDVImax, NDVImedian, NDVImin NDWImax, NDWImedian LSTmax |
Lasso | 4 | 1 | NDVImean NDWImean, NDWImedian |
MIDTR | 12 | 3 | EVImax, EVImedian, EVImin NDVImax, NDVImedian, NDVImin NDWImax, NDWImean, NDWImin LSTmin, LSTmedian |
ImprovedRFECV | 12 | 5 | EVImedian, EVImean NDVImax, NDVImedian, NDVImean NDWImedian, NDWImean, NDWImin |
MSFS (Ours) | 12 | 2 | EVImax, EVImedian, EVImin NDVImax, NDVImedian, NDVImin NDWImax, NDWImin |
Experiment | MI | SFS | Lasso | MIDTR | ImprovedRFECV | MSFS (Ours) |
---|---|---|---|---|---|---|
1 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
2 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
3 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
4 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
5 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
6 | 0.906 | 0.918 | 0.884 | 0.869 | 0.906 | 0.909 |
7 | 0.908 | 0.918 | 0.884 | 0.862 | 0.904 | 0.909 |
8 | 0.908 | 0.918 | 0.884 | 0.869 | 0.904 | 0.909 |
9 | 0.907 | 0.918 × | 0.884 | 0.864 | 0.905 | 0.909 |
10 | 0.908 | 0.918 × | 0.884 | 0.869 | 0.904 | 0.909 |
Experiment | Non-FS (24) | MI (16) | SFS (15) | Lasso (4) | MIDTR (12) | ImprovedRFECV (12) | MSFS (ours) (12) |
---|---|---|---|---|---|---|---|
1 | 63 | 68 ↑ | 81 ↑ | 85 ↑ | 75 ↑ | 71 ↑ | 78 ↑ |
2 | 62 | 66 ↑ | 70 ↑ | 84 ↑ | 68 ↑ | 38 ↓ | 71 ↑ |
3 | 63 | 63 | 42 ↓ | 88 ↑ | 59 ↓ | 67 ↑ | 68 ↑ |
4 | 64 | 73 ↑ | 68 ↑ | 86 ↑ | 75 ↑ | 71 ↑ | 78 ↑ |
5 | 64 | 66 ↑ | 74 ↑ | 87 ↑ | 71 ↑ | 76 ↑ | 77 ↑ |
6 | 54 | 66 ↑ | 70 ↑ | 87 ↑ | 73 ↑ | 71 ↑ | 71 ↑ |
7 | 48 | 69 ↑ | 71 ↑ | 88 ↑ | 61 ↑ | 68 ↑ | 71 ↑ |
8 | 52 | 66 ↑ | 70 ↑ | 87 ↑ | 68 ↑ | 70 ↑ | 71 ↑ |
9 | 63 | 76 ↑ | 80 ↑ | 88 ↑ | 71 ↑ | 71 ↑ | 79 ↑ |
10 | 63 | 73 ↑ | 80 ↑ | 88 ↑ | 75 ↑ | 75 ↑ | 78 ↑ |
Average | 59.60 | 68.60 ↑ | 70.60 ↑ | 86.80 ↑ | 69.60 ↑ | 67.80 ↑ | 74.56 ↑ |
Improvement | 9.00 | 11.00 | 27.20 | 10.00 | 8.20 | 14.96 |
Algorithm | Evaluation Metrics | ||||
---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | ROC_AUC | |
Non-FS | 0.927 | 0.889 | 0.861 | 0.875 | 0.908 |
MI | 0.923 | 0.885 | 0.852 | 0.868 | 0.903 |
SFS | 0.927 | 0.893 | 0.857 | 0.875 | 0.907 |
Lasso | 0.904 | 0.856 | 0.814 | 0.835 | 0.878 |
MIDTR | 0.891 | 0.896 | 0.715 | 0.795 | 0.842 |
ImprovedRFECV | 0.924 | 0.880 | 0.860 | 0.870 | 0.905 |
MSFS (ours) | 0.931 | 0.903 | 0.859 | 0.881 | 0.910 |
Algorithm | Evaluation Metrics | Average | ||||
---|---|---|---|---|---|---|
Accuracy | Precision | Recall | F1-Score | ROC_AUC | ||
Non-FS | 0.35 | 0.73 | 0.80 | 0.72 | 0.55 | 0.63 |
MI | 0.12 | 0.25 | 0.50 | 0.25 | 0.32 | 0.29 |
SFS | 0.30 | 1.00 | 0.90 | 0.40 | 0.40 | 0.60 |
Lasso | 1.10 | 1.67 | 2.57 | 1.95 | 1.55 | 1.77 |
MIDTR | 0.25 | 1.78 | 2.62 | 0.90 | 0.82 | 1.27 |
ImprovedRFECV | 0.15 | 0.35 | 0.68 | 0.22 | 0.50 | 0.38 |
MSFS (ours) | 0.10 | 0.20 | 0.05 | 0.10 | 0.00 | 0.09 |
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Su, Y.; Zhao, L.; Li, H.; Li, X.; Chen, J.; Ge, Y. An Efficient Task Implementation Modeling Framework with Multi-Stage Feature Selection and AutoML: A Case Study in Forest Fire Risk Prediction. Remote Sens. 2024, 16, 3190. https://doi.org/10.3390/rs16173190
Su Y, Zhao L, Li H, Li X, Chen J, Ge Y. An Efficient Task Implementation Modeling Framework with Multi-Stage Feature Selection and AutoML: A Case Study in Forest Fire Risk Prediction. Remote Sensing. 2024; 16(17):3190. https://doi.org/10.3390/rs16173190
Chicago/Turabian StyleSu, Ye, Longlong Zhao, Hongzhong Li, Xiaoli Li, Jinsong Chen, and Yuankai Ge. 2024. "An Efficient Task Implementation Modeling Framework with Multi-Stage Feature Selection and AutoML: A Case Study in Forest Fire Risk Prediction" Remote Sensing 16, no. 17: 3190. https://doi.org/10.3390/rs16173190