Grey decision rules for interval MADA based on rough set theory

Based on rough set theory, a new decision rule for information system with interval numbers is proposed. First the interval values are discretized through an improved rough clustering algorithm. Then the redundant set of attributes is obtained by constituting homogenous matrix. Then, after a part of decision rules have been generated, we propose grey decision rules that are useful in inducing rules after referring to preference-classified data tables based on grey relational analysis. To obtain weights of attribute, the reciprocal matrix which can avoid the influence of subjective factors, is constituted according to the definition of relative significance between two attributes, and then an optimal model connected with the reciprocal matrix is solved by genetic algorithm. Through contrastive analysis with back propagation (BP) neural network on stapling training planes, it is shown that the grey decision rules are more efficient than BP neural network.