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Hyperparameter Optimization for Landslide Susceptibility Mapping: A Comparison between Baseline, Bayesian and Metaheuristic Hyperparameter Optimization Techniques for Machine Learning Algorithms
Abbas, F.; Zhang, F.; Ismail, M.; Khan, G.; Iqbal, J.; Alrefaei, A.F.; Albeshr, M.F. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques. Sensors2023, 23, 6843.
Abbas, F.; Zhang, F.; Ismail, M.; Khan, G.; Iqbal, J.; Alrefaei, A.F.; Albeshr, M.F. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques. Sensors 2023, 23, 6843.
Abbas, F.; Zhang, F.; Ismail, M.; Khan, G.; Iqbal, J.; Alrefaei, A.F.; Albeshr, M.F. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques. Sensors2023, 23, 6843.
Abbas, F.; Zhang, F.; Ismail, M.; Khan, G.; Iqbal, J.; Alrefaei, A.F.; Albeshr, M.F. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques. Sensors 2023, 23, 6843.
Abstract
Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For example, the metaheuristic algorithm improved the overall accuracy of the random forest model. Additionally, Bayesian algorithms, such as Gaussian processes, performed well for models like KNN and SVM. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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