Weight Acquisition of Fuzzy Production Rules Based on Minimum Entropy Principle
Yang Feng · Computer Engineering and Applications Journal · 2006
Weighted Fuzzy Production Rules(WFPRs) is a fundamental and important way of imprecise knowledge representation.Generally speaking,the usual criterion of the weight values adjustment,which is based only on improving training accuracy,often results in an over-fitting.In order to overcome the shortcoming of over-fitting and improve the generalization capability of WFPRs,this paper proposes a new criterion based on the well-known Minimum Entropy Principle(MEP),presents a mathematics model for acquiring the weight values,and shows a Genetic Algorithm to solve this model.The experimental results show that the training and testing accuracy will increase when the value of fuzzy entropy on the training set decreases,and the weights acquired according to MEP can lead to an enhancement of generalization capability of WFPRs for selected databases.