Fuzzy decision tree induction based on optimization of parameters
Juan Sun · Computer Engineering and Applications Journal · 2012
Fuzzy Decision Tree induction(FDT)has been used in more and more application area.When the data are numerical,the FDT algorithms need to fuzzify them into some linguistic items.That how many linguistic items of an attribute are proper is not known.The selection generally depends on experts'opinion or people's common.Currently,it is not yet available to learn the number of linguistic items by using the experimental method of particle swarm optimization.The paper introduces a PSO based approach to optimize the selection of linguistic item's number in fuzzifying processing of data in FDT(FDT-K algorithm).Experimental studies show that the FDT-K algorithm compared with the people's common methods to decide the number of linguistic items of attribute can create a better fuzzy decision tree with higher classification and generalization capability.