Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33546
Title: Web mining for improving risk assessment in port state control inspection
Authors: Xu, R
Lu, Q 
Li, KX
Li, W 
Keywords: Web sites
Data mining
Database management systems
Feature extraction
Inspection
Marine engineering
Marine safety
Pattern matching
Risk management
Support vector machines
Issue Date: 2007
Publisher: IEEE
Source: International Conference on Natural Language Processing and Knowledge Engineering, 2007 : NLP-KE 2007, August 30 2007-September 1 2007, Beijing, p. 427-434 How to cite?
Journal: International Conference on Natural Language Processing and Knowledge Engineering, 2007 : NLP-KE 2007, August 30 2007-September 1 2007, Beijing 
Abstract: Port state control (PSC) inspection is the most important mechanism to ensure world marine safety. Existing PSC risk assessment systems estimate the risk of each candidate ship on the target factors, which is recorded in the inspection database, to help the port authorities identify ships at high risks. The performance of these systems is difficult to be improved due to the limited available factors. This paper presents an improved risk assessment system, which is strengthened by web mining technique. This system employs profile-based wrapper to extract inspection details from inspection report web pages and adopts a template-matching-based method to extract new target features from deficiency details. By incorporating new target features, the risk assessment system based on Support Vector Machine is improved. Experimental results have shown that the new system improves the risk assessment accuracy effectively.
URI: http://hdl.handle.net/10397/33546
ISBN: 978-1-4244-1610-3
978-1-4244-1611-0 (E-ISBN)
DOI: 10.1109/NLPKE.2007.4368066
Appears in Collections:Conference Paper

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