The Study of Port Logistics Capabilities Based on Random Forest

Zhen Zhong Ge; Yong Jun Liu

doi:10.4028/www.scientific.net/AMM.58-60.1701

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60The Study of Port Logistics Capabilities Based on...

The Study of Port Logistics Capabilities Based on Random Forest

Abstract:

The throughput of port is an important indicator of port logistics capabilities. Behind the throughput there are a lot of factors. It has practical significance for quantitative analysis these factors that the degree they affect the throughput. This can be calculated the order of importance of these factors, then we can improve the focus factor, thereby enhance the throughput and logistics capabilities according to the port development plans.

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Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

1701-1706

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.1701

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Online since:

June 2011

Authors:

Zhen Zhong Ge, Yong Jun Liu

Keywords:

Development Strategy, Logistics Capabilities, Order of Importance, Random Forest (RF), Throughput

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References

[1] Leo Breiman, Random Forests, Machine Learning, 2001, 45 (1): 5 ~32.

Google Scholar

[2] Jong Oh, Mark Laubach, Artur Luczak. Estimating neuronal variable importance with Random Forest, Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference, 33~34.

DOI: 10.1109/nebc.2003.1215978

Google Scholar

[3] Liaw A, Wiener M, Classification and regression by RandomForest, Rnews, 2002, 2: 18–22.

Google Scholar

[4] Wang Dan, Yang Zan , Analysis of Influential Factors of Port Throughput, Port & Waterway Engineering 2007, 1: 45 ~ 48.

Google Scholar

[5] Wang Zhihong, Wang Huazhen, Fund Evaluation Model Selection based on Random Forest, Accounting and Finance, 2009, 1: 65 ~ 70.

Google Scholar

[6] Ren WeiZhen, International logistics center in Tianjin Binhai New Area Status and Strategy Construction, Economic Outlook the Bohai Sea, 2008, 10: 43 ~ 46.

Google Scholar