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Proceedings Paper

An intelligent diagnosis model based on rough set theory
Author(s): Ze Li; Hong-Xing Huang; Ye-Lu Zheng; Zhou-Yuan Wang
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Paper Abstract

Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87842L (13 March 2013); doi: 10.1117/12.2021228
Show Author Affiliations
Ze Li, Sci-tech Information Institute (China)
Sun Yat-Sen Univ. (China)
Hong-Xing Huang, Sci-tech Information Institute (China)
Ye-Lu Zheng, Sci-tech Information Institute (China)
Zhou-Yuan Wang, Sci-tech Information Institute (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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