Intelligent Recognition of Extension Group Decision-making under Uncertainty

Regarding to the uncertain extension group decision making, this paper combine the rough set and extension set to utilize both of their advantages. Based on extension transformation, we discuss the attribute-dimension reduction of uncertain extension group decision making and rule extraction. In addition, correlation based core data range and extendable data range are also studied and analyzed to gain multiple-strategic rough classification of decision objects and dynamic recognition of system decision making problems under uncertain condition. The results showed that this method outperforms others in terms of data classification, filtering, rule extraction and strategy recognition, especially when sample size are small, uncertain and descriptive.