Fuzzy weighted classification rules induction from data
Eric C.C. Tsang, Hongbing Li, D.S. Yeung, J.W.T. Lee · 2002
One popular approach for automatic generation of fuzzy classification rules is decision tree induction, but almost all of the existing decision tree induction methods have not considered the importance of each proposition in the antecedent (i.e. the weight) contributing to the consequent. Unfortunately, this weight plays an important role in many real world problems. We present an effective approach for learning fuzzy weighted classification rules from data. The weights for each rule antecedent propositions will be assigned based on a relative weight matrix. Some experiments are conducted and the results show that this approach usually can obtain a compact set of fuzzy rules and considerable classification accuracy, especially, the learning accuracy can be improved by incorporating the weight.