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New Technology of Library and Information Service  2011, Vol. 27 Issue (3): 73-79    DOI: 10.11925/infotech.1003-3513.2011.03.12
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A New Classifier Design in a Topic Search Engine by Combining Multi-layer Classifier with Naive Bayes Classification Model
Zhang Hongbin, Cao Yiqin
School of Software, East China Jiaotong University, Nanchang 330013, China
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Abstract  The paper firstly analyzes the distribution characteristics of computer education resources on Web, then it designs a multi-layer classifier to resolve the topic classification problem in topic crawling procedure by combining topic words and resources forms, and introduces how to make the precise classification fusion by Naive Bayes Classifier model and how the resources are stored correctly into the hard disk. Finally, experiment results show that the key design idea is feasible and many performances are acceptable, such as the avarage accuracy of the topic classification algorithm reaches to 78% as well as the avarage recall accuracy reaches to 61% and the avarage resources parsing accuracy reaches to 81.5%.
Key wordsMulti-layer classifier      Topic search engine      Computer education resources      Naive Bayes     
Received: 17 January 2011      Published: 05 May 2011



Cite this article:

Zhang Hongbin, Cao Yiqin. A New Classifier Design in a Topic Search Engine by Combining Multi-layer Classifier with Naive Bayes Classification Model. New Technology of Library and Information Service, 2011, 27(3): 73-79.

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