Improved Decision Tree Attribute Selection Method Based on the Contribution Factor

In this paper,the calculation of information entropy of the traditional ID3 algorithm by approximate transformation is simplified.The contribution factor is introduced in this paper to improve the algorithm,aiming at two shortcuts of ID3 algorithm,that is,tending to select more value attributes and have exclusion of attributes whose values are uniformly distributed.It allows the attribute selection method to select division attributes in balance.Through experiment testing,the improved attribute selection method can reduce the classification time,optimize the classification results and reflect the actual situation.