Decision Tree Manipulation of Machine Learning Procedure In Underwriting Task

Decision tree manipulation is one of the useful approaches for extracting classification knowledge. The most popular dynamic knowledge used in the decision tree generation is the minimum entropy. This dynamic knowledge has a serious disadvantage-the poor generalization capability. Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. It has good generalization. Considering the relationship between the classification margin of support vector machine (SVM) and the generalization capability, the large margin of SVM can be used as the dynamic knowledge of decision tree, in order to improve its generalization capability. Comparing with the binary decision tree using the minimum entropy as the dynamic knowledge, the task show that the generalization capability has been improved by using the new dynamic. Then the in complete database of insurance company is mined by the data mining’s association rule procedure. Thirdly the Support Vector Machine (SVM) is applied to the underwriting task to classify the applicants. Finally the directions for improving this procedure is pointed out. The procedure proposed in this paper has promising future in underwriting task.