Appling Data Mining Technology to Analasis Clearance Efficiency in the Port Logistics

Abstract:

Currently, monitoring customs declaration with limited examination of imported goods by available scarce resources poses considerable challenge to the customs authority worldwide. This a positive impact on international trade and foreign investment of a country to find out the limited factors of customs clearance, put forward the improved solution and enhance the efficiency of customs clearance efficiency of port logistics. This paper presents a classification model, which is a sort of data mining technology, to analyze the risk of commodity through customs clearance, and builds a classifier of customs inspection as the reference for customs inspection and monitoring. And the classification model based on BP neural network is established and evaluated through experiments, which are proved that a classification data mining method can be used for risk evaluation on customs clearance business to improve the customs inspection and monitoring.

• Periodical: Key Engineering Materials (Volumes 480-481)
• Pages: 1144-1149
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.480-481.1144
• Online since: June 2011
• Authors: Ya Qin Wang, Yu Ming Song
• Keywords: Clearance Efficiency, Custom, Data Mining (DM), Port Logistics

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