Application of Bayesian Decision Tree Algorithm in Breast Cancer Prediction - Xiang, Yang; Dong, Lin-Lu; Song, Hong; Yu, Kun-jian (Journal of Vibration Testing and System Dynamics, 2026)
Aiming at the low classification accuracy of common decision tree C4.5 algorithm and the condition attribute problem of decision tree splitting point, a Bayesian decision tree algorithm combining ordinary decision tree C4.5 algorithm and naive Bayesian correlation algorithm is proposed in this paper. On this basis, the consistency test coefficient(kappa coefficient) is introduced to improve the correlation algorithm in the information gain rate in C4.5. Finally, the newly generated decision tree is pruned according to the most suitable splitting point. The improved algorithm proposed in this paper has a distinct improvement compared with the algorithm proposed in other literatures considering both time and accuracy.