Decision Tree Classification Based on Naive Bayesian and ID3 Algorithm
Huang Yuda, Yiran Wang · Jisuanji gongcheng · 2012
This paper proposes an improved decision tree classification algorithm based on naive Bayes algorithm and ID3 algorithm.It introduces objective attribute importance parameter,gives a kind of conditional independence assumption that is weaker than naive Bayesian algorithm,and uses the weighted independent information entropy as splitting attribute's selection criteria.Theoretical analysis and experimental results show that the improved algorithm,to a certain extent well overcomes ID3 algorithm's shortcoming of multi-value tendency,and improves algorithm's implementation efficiency and classification accuracy.