Breeding Decision Tree Using Genetic Algorithm And Building Credit Analysis System To Analyze Bank Custom Credit Risk

In the custom credit analysis, decision tree is always used to separate the custom because of the large amount of nominal variables. Achieving the global best tree is an NP-hard problem, so greedy method is the most common and fast way to get a relative good solution yet seldom the global best one. The new method in this paper provides a brand new idea to breed the decision tree using genetic algorithm to get a relative good tree during the acceptable time. And then, combined with logistic regression and multi-layer perception, a credit analysis system will be generated.