|Table of Contents|

[1] Zhang Weifeng, , Xu Baowen, et al. Document classification approachby rough-set-based corner classification neural network [J]. Journal of Southeast University (English Edition), 2006, 22 (3): 439-444. [doi:10.3969/j.issn.1003-7985.2006.03.032]
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Document classification approachby rough-set-based corner classification neural network()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
22
Issue:
2006 3
Page:
439-444
Research Field:
Automation
Publishing date:
2006-09-30

Info

Title:
Document classification approachby rough-set-based corner classification neural network
Author(s):
Zhang Weifeng1;  2;  3;  Xu Baowen2;  3;  Cui Zifeng2;  3;  Xu Junling 2;  3
1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2College of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3Jiangsu Institute of Software Quality, Nanjing 210096, China
Keywords:
document classification;  neural network;  rough set;  meta search engine
PACS:
TP183
DOI:
10.3969/j.issn.1003-7985.2006.03.032
Abstract:
A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding.In the Rough-CC4, the documents are described by the equivalent classes of the approximate words.By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words.In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded.By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced.The Rough-CC4 can be used in automatic classification of documents.

References:

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Memo

Memo:
Biographies: Zhang Weifeng(1975—), male, doctor, associate professor, wfzhang@yahoo.com;Xu Baowen(1961—), male, doctor, professor, bwxu@seu.edu.cn.
Last Update: 2006-09-20