This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules.The method includes two phases:(1) automatic generation of the membership functio...This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules.The method includes two phases:(1) automatic generation of the membership function,and (2) use of the corresponding linguistic data to extract classification rules.The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics,and generate the fuzzy membership functions automatically.Experimental results show that the proposed method surpasses traditional methods in accuracy.展开更多
文摘This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules.The method includes two phases:(1) automatic generation of the membership function,and (2) use of the corresponding linguistic data to extract classification rules.The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics,and generate the fuzzy membership functions automatically.Experimental results show that the proposed method surpasses traditional methods in accuracy.
