Ship detention prediction via feature selection scheme and support vector machine (SVM)
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DOI: 10.1080/03088839.2021.1875141
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Cited by:
- Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
- Xizi Qiao & Ying Yang & King-Wah Pang & Yong Jin & Shuaian Wang, 2024. "Ship Selection and Inspection Scheduling in Inland Waterway Transport," Mathematics, MDPI, vol. 12(15), pages 1-23, July.
- Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
- Yan, Ran & Wang, Shuaian & Zhen, Lu, 2023. "An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
- Xizi Qiao & Ying Yang & Yu Guo & Yong Jin & Shuaian Wang, 2024. "Optimal Routing and Scheduling of Flag State Control Officers in Maritime Transportation," Mathematics, MDPI, vol. 12(11), pages 1-23, May.
- Xuecheng Tian & Shuaian Wang, 2022. "Cost-Sensitive Laplacian Logistic Regression for Ship Detention Prediction," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
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