Heuristic Model and Application of Decision Tree Based on Quasi-Linear Criterion

Decision tree, as a simple classification algorithm, is an effective tool for mining knowledge rules, and it has been successfully applied in many fields. Based on the selection problems of the expanded attributes, we put forward quasi-linear leaf criterion and data utilization criterion which can recognize the extension ability of attributes, and give a selection model of expanded attributes based on quasi-linear criterion (denoted by QASM for short). Then we compare and analyze the performance of QASM combining with ID3 algorithm through an example. The results show that QASM can not only effectively merge the decision consciousness into decision-making process, but also the computational complexity is far below that of ID3 algorithm, and has wide application and operability.