DocumentCode :
3331882
Title :
Algorithm on rule extraction based on rough set and neural network theory
Author :
Zhang Shao Bing ; Ji Yan Fu
Author_Institution :
Comput. & Inf. Eng. Coll., Heilongjiang Inst. of Sci. & Technol., Harbin, China
Volume :
2
fYear :
2011
fDate :
22-24 Aug. 2011
Firstpage :
1137
Lastpage :
1140
Abstract :
Rough set method could obtain the acceptable classification pattern through removing redundant information based on no additional information given in advance. Neural network had strong self-organization and noise suppression capability. This paper proposed a method for rule extraction based on rough set and neural network combining both advantages. Firstly this paper dispersed initial data set and initiatively reduced condition attributes of decision-making table using rough set, then learned and forecasted data using neural network and filtrated noises of decision-making table through deleting unclassified data, finally extracted rules using value reduction algorithm of rough set. The experiment proves that this method is quick and effective, and can remain high robustness of neural network avoiding the difficulty to extract rules from neural network compared to traditional rule extraction algorithms.
Keywords :
decision making; decision tables; neural nets; pattern classification; rough set theory; decision making table; neural network theory; noise suppression capability; pattern classification; rough set theory; rule extraction algorithm; self-organization capability; value reduction algorithm; Accuracy; Classification algorithms; Data mining; Decision making; Noise; Set theory; Training; attribute reduction; neural network; rough set; rule extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Strategic Technology (IFOST), 2011 6th International Forum on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-4577-0398-0
Type :
conf
DOI :
10.1109/IFOST.2011.6021221
Filename :
6021221
Link To Document :
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