Influence diagrams is getting extensively applied as rule acquisition in economics, business, and military fields, in traditional influence diagrams, computational models of dependence relations are established by probability distribution, which has good performance in deterministic information system. However, information systems(IS) in real world usually are uncertain or approximate, such as IS composed by interval valued data, rules from large scaled uncertain data is also imprecise, the suitability of probability distribution model is questioned, how to represent approximate knowledge is a focus issue of traditional influence diagram model. Grey sets and rough sets are two mathematical tools in dealing with uncertain IS, consequently, this paper proposes a new alternative computational model grey rough influence diagrams to solve that problem, it is a combination of grey rough sets and influence diagrams. In the proposed scheme, the random relationship between the nodes and approximate rules are denoted by grey rough sets from uncertain IS. This paper provides a general influence diagrams model for uncertain IS, finally, a decision analysis example in medical fields is illustrated.
['Haiqing Hu', 'Bingqiang Liu', 'Tao Shen']
PGrey rough influence diagrams method and its medical application