On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice

Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed....
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Autor*in:	Mahdi Aghaabbasi [verfasserIn] Mujahid Ali [verfasserIn] Michal Jasinski [verfasserIn] Zbigniew Leonowicz [verfasserIn] Tomas Novak [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 19762-19774
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:19762-19774

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3247448

Katalog-ID:	DOAJ088332330

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spelling	10.1109/ACCESS.2023.3247448 doi (DE-627)DOAJ088332330 (DE-599)DOAJd58d24fc52684882a4c949c71ee3f166 DE-627 ger DE-627 rakwb eng TK1-9971 Mahdi Aghaabbasi verfasserin aut On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use. Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice Electrical engineering. Electronics. Nuclear engineering Mujahid Ali verfasserin aut Michal Jasinski verfasserin aut Zbigniew Leonowicz verfasserin aut Tomas Novak verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 19762-19774 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:19762-19774 https://doi.org/10.1109/ACCESS.2023.3247448 kostenfrei https://doaj.org/article/d58d24fc52684882a4c949c71ee3f166 kostenfrei https://ieeexplore.ieee.org/document/10049559/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 19762-19774
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allfieldsGer	10.1109/ACCESS.2023.3247448 doi (DE-627)DOAJ088332330 (DE-599)DOAJd58d24fc52684882a4c949c71ee3f166 DE-627 ger DE-627 rakwb eng TK1-9971 Mahdi Aghaabbasi verfasserin aut On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use. Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice Electrical engineering. Electronics. Nuclear engineering Mujahid Ali verfasserin aut Michal Jasinski verfasserin aut Zbigniew Leonowicz verfasserin aut Tomas Novak verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 19762-19774 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:19762-19774 https://doi.org/10.1109/ACCESS.2023.3247448 kostenfrei https://doaj.org/article/d58d24fc52684882a4c949c71ee3f166 kostenfrei https://ieeexplore.ieee.org/document/10049559/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 19762-19774
allfieldsSound	10.1109/ACCESS.2023.3247448 doi (DE-627)DOAJ088332330 (DE-599)DOAJd58d24fc52684882a4c949c71ee3f166 DE-627 ger DE-627 rakwb eng TK1-9971 Mahdi Aghaabbasi verfasserin aut On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use. Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice Electrical engineering. Electronics. Nuclear engineering Mujahid Ali verfasserin aut Michal Jasinski verfasserin aut Zbigniew Leonowicz verfasserin aut Tomas Novak verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 19762-19774 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:19762-19774 https://doi.org/10.1109/ACCESS.2023.3247448 kostenfrei https://doaj.org/article/d58d24fc52684882a4c949c71ee3f166 kostenfrei https://ieeexplore.ieee.org/document/10049559/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 19762-19774
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source	In IEEE Access 11(2023), Seite 19762-19774 volume:11 year:2023 pages:19762-19774
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topic_facet	Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Mahdi Aghaabbasi @@aut@@ Mujahid Ali @@aut@@ Michal Jasinski @@aut@@ Zbigniew Leonowicz @@aut@@ Tomas Novak @@aut@@
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callnumber-first	T - Technology
author	Mahdi Aghaabbasi
spellingShingle	Mahdi Aghaabbasi misc TK1-9971 misc Bayesian optimization algorithm misc hyperparameters misc sustainable mode choice decision misc work travel mode choice misc Electrical engineering. Electronics. Nuclear engineering On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
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topic_title	TK1-9971 On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice Bayesian optimization algorithm hyperparameters sustainable mode choice decision work travel mode choice
topic	misc TK1-9971 misc Bayesian optimization algorithm misc hyperparameters misc sustainable mode choice decision misc work travel mode choice misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Bayesian optimization algorithm misc hyperparameters misc sustainable mode choice decision misc work travel mode choice misc Electrical engineering. Electronics. Nuclear engineering
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title	On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
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title_sort	on hyperparameter optimization of machine learning methods using a bayesian optimization algorithm to predict work travel mode choice
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title_auth	On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
abstract	Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.
abstractGer	Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.
abstract_unstemmed	Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.
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title_short	On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
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