Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty
Decision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today’s big data age, while the predict-then-optimize framework has be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/17/3782 |
| _version_ | 1797582142281089024 |
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| author | Xuecheng Tian Yanxia Guan Shuaian Wang |
| author_facet | Xuecheng Tian Yanxia Guan Shuaian Wang |
| author_sort | Xuecheng Tian |
| collection | DOAJ |
| description | Decision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today’s big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-making quality. To enhance predictive accuracy and consequent decision-making quality, we introduce a data transformation technique into the predict-then-optimize framework. Our approach transforms target values in linear regression, decision tree, and random forest models using a power function, aiming to boost their predictive prowess and, in turn, drive better decisions. Empirical validation on several datasets reveals marked improvements in decision tree and random forest models. In contrast, the benefits of linear regression are nuanced. Thus, while data transformation can bolster the predict-then-optimize framework, its efficacy is model-dependent. This research underscores the potential of tailoring transformation techniques for specific models to foster reliable and robust decision-making under uncertainty. |
| first_indexed | 2024-03-10T23:17:43Z |
| format | Article |
| id | doaj.art-c6298a77174b4dc4b886819db313fbef |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:17:43Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c6298a77174b4dc4b886819db313fbef2023-11-19T08:32:04ZengMDPI AGMathematics2227-73902023-09-011117378210.3390/math11173782Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under UncertaintyXuecheng Tian0Yanxia Guan1Shuaian Wang2Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong KongDepartment of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong KongDepartment of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong KongDecision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today’s big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-making quality. To enhance predictive accuracy and consequent decision-making quality, we introduce a data transformation technique into the predict-then-optimize framework. Our approach transforms target values in linear regression, decision tree, and random forest models using a power function, aiming to boost their predictive prowess and, in turn, drive better decisions. Empirical validation on several datasets reveals marked improvements in decision tree and random forest models. In contrast, the benefits of linear regression are nuanced. Thus, while data transformation can bolster the predict-then-optimize framework, its efficacy is model-dependent. This research underscores the potential of tailoring transformation techniques for specific models to foster reliable and robust decision-making under uncertainty.https://www.mdpi.com/2227-7390/11/17/3782uncertain decision makingpredict-then-optimizedata transformationdata-driven optimization |
| spellingShingle | Xuecheng Tian Yanxia Guan Shuaian Wang Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty Mathematics uncertain decision making predict-then-optimize data transformation data-driven optimization |
| title | Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty |
| title_full | Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty |
| title_fullStr | Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty |
| title_full_unstemmed | Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty |
| title_short | Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty |
| title_sort | data transformation in the predict then optimize framework enhancing decision making under uncertainty |
| topic | uncertain decision making predict-then-optimize data transformation data-driven optimization |
| url | https://www.mdpi.com/2227-7390/11/17/3782 |
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