DocumentCode :
468176
Title :
ROGAND: A Discretization Model
Author :
Shang, Lin ; Yu, Shaoyue ; Jia, Xiuyi ; Ji, Yangsheng
Author_Institution :
Nanjing Univ., Nanjing
Volume :
1
fYear :
2007
fDate :
24-27 Aug. 2007
Firstpage :
655
Lastpage :
659
Abstract :
A discretization model called ROGAND ( ROugh sets, Genetic Algorithm and Neural network based Discretization ) is presented in the paper, which combines Rough set theory with the genetic algorithm to build a four-layer neural network. This model consists of the data preprocessor (DP), the discretization module(DM) and the optimization module (OM). The discretized intervals obtained through the ROGAND model is independent on the candidates of cut-point sets and the denoted values can be more precise. The experiments indicate that the method is effective and the output cut-points are accurate and easy-set.
Keywords :
data analysis; genetic algorithms; neural nets; rough set theory; ROGAND; data preprocessor; discretization model; discretization module; genetic algorithm; neural network; optimization module; rough set theory; Bayesian methods; Data mining; Delta modulation; Genetic algorithms; Laboratories; Machine learning; Neural networks; Paper technology; Rough sets; Set theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
Type :
conf
DOI :
10.1109/FSKD.2007.492
Filename :
4406005
Link To Document :
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