Design and Implementation of a Multilayer Classifier Based on Rough Set
Amoi Electronics · Computer Engineering and Applications Journal · 2006
A new scheme of knowledge encoding in a multilayer classifier using rough set theoretic concepts is described in this paper.Rough Set theory has a powerful capability for qualitative analysis,while BP neural networks can approach most problems with a much satisfying accuracy.By combining those advantages of the two theories,we can construct a kind of neural network with good understandability,simple computation and exact accuracy.Firstly,Rough set theory is utilized for extracting crude domain knowledge.Then some rules are generated from a decision table by computing relative reduct.The dependency factors of these rules are encoded as the initial connection weights of the network.The network is next trained to refine its weight values.Finally,an example with satisfying results is also presented in this paper.