An intelligent diagnosis model based on rough set theory

Ze Li; Hong-Xing Huang; Ye-Lu Zheng; Zhou-Yuan Wang

doi:10.1117/12.2021228

13 March 2013 An intelligent diagnosis model based on rough set theory

Ze Li, Hong-Xing Huang, Ye-Lu Zheng, Zhou-Yuan Wang

Proceedings Volume 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies; 87842L (2013) https://doi.org/10.1117/12.2021228
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

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.

Citation Download Citation

Ze Li, Hong-Xing Huang, Ye-Lu Zheng, and Zhou-Yuan Wang "An intelligent diagnosis model based on rough set theory", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87842L (13 March 2013); https://doi.org/10.1117/12.2021228

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