Abstract: The present invention provides an optimal feature subset selection method in credit scoring based on Informedness coefficient. The present invention aims to solve the problem that the existing credit scoring system cannot ensure the strongest overall default identification ability and does not consider the correlation among features when selecting a set of features. With the maximum default identification ability of the Informedness coefficient of the credit score as the standard for optimizing a feature subset, with the decision variable that whether the feature is selected into a feature subset, with the maximum default identification ability of the Informedness coefficient as the objective function, and with the constraint condition that features reflecting information redundancy cannot be simultaneously selected to establish a 0-1 programming model, the optimal feature subset in credit scoring is selected.

Abstract: A method for optimizing a credit rating indicator group based on the maximum default identification ability measured by Fisher Score is disclosed. The maximum default identification ability measured by Fisher Score of the credit score is used as the standard for optimizing an indicator group. After the indicators reflecting information redundancy are removed, the Fisher Score values of all the indicator groups formed are compared by the traversing method, and the group of indicators with the maximum Fisher Score value of the default identification ability of the credit score is selected as the optimal indicator group. The method of the invention ensures the maximum overall default identification ability measured by Fisher Score of the credit rating system, and provides a decision basis for all investors such as banks and individuals to effectively identify credit risks.

Abstract: A method for determining optimal weight vector of credit rating based on the maximum default identification ability measured by approaching ideal points is disclosed. The minimum algebraic sum of the Euclidean distances from credit scores of a non-default enterprise to a positive ideal point and the minimum algebraic sum of the Euclidean distances from credit scores of a default enterprise to a negative ideal point are taken as the first objective function, and the lowest dispersion degree of the “distances from scores of a non-default enterprise to a positive ideal point” and the lowest dispersion degree of the “distances from scores of a default enterprise to a negative ideal point” are taken as the second objective function to construct multi-objective programming functions and derive a group of optimal weights of a credit rating equation.

Abstract: The invention supplies to an optimal credit rating division method based on maximizing credit similarity, which belongs to the field of credit services technology. The invention provides a credit rating method, which meets the essential attribute of credit that the higher credit rating comes with the lower corresponding LGD, and ensures that customers with big credit status difference are divided into the different level and customers with similar credit status are divided into the same level. This invention constructs a nonlinear programming model to divide the credit rating based on maximizing credit similarity, whose objective function aims at minimizes the deviation of credit scores within the group, and maximum the deviation of credit scores between groups, with the constraint that the LGD is strictly increasing with credit rating from high to low.