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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Application of Bayesian Decision Tree Algorithm in Breast Cancer Prediction

Journal of Vcibration Testing and System Dynamics 4(1) (2020) 43--49 | DOI:10.5890/JVTSD.2020.03.002

Yang Xiang, Lin-Lu Dong, Hong Song, Kun-jian Yu

School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China

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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.


  1. [1]  Weng, T. (2018), Application of decision tree machine learning algorithm in breast cancer diagnosis, Communication World, 10, 224-226.
  2. [2]  He, Q., Zhao, G., Ju, Y., Zhou, W., Li, M., Dong, Q., and Zhao, K. (2019), Application of machine learning algorithm in diabetes prediction, Journal of Guizhou University, (Natural Science Edition), 36(2), 65-68.
  3. [3]  An, P., Cheng, X., and Liu, Y. (2019), Application of Fleiss’ kappa coefficient in Bayesian decision tree algorithm, Computer Engineering and Applications, 2, 1-7.
  4. [4]  Wu, S.B., Chen, Z.G., and Huang, R. (2016), ID3 optimization algorithm based on correlation coefficient, Computer Engineering and Science, 38(11), 2342-2347.
  5. [5]  Chen, Y., Ma, Z.B., and Huang, M. (2013), Optimized C4.5 decision tree algorithm, Software, 34(2), 61-64.
  6. [6]  Lu, H., Wen, J., and Li, Z.S. (2019), An assertive reasoning method for emergency response management based on knowledge elements C4.5 decision tree, Expert Systems With Applications, 122, 389-395.
  7. [7]  Zheng, W. and Ma, N. (2015), An improved decision tree post-pruning algorithm, Computer and Digital 156 Engineering, 43(6), 960-971.
  8. [8]  Wang, Q.Z. (2011), Research and solution of over-fitting in decision tree algorithm, Journal of Yuncheng University, 29(2), 53-57.
  9. [9]  Yang, L., Cao, C.L., and Sun, J.G. (2017), Research on improved naive Bayesian algorithm in spam filtering, Transactions of Communications, 38(4), 140-148.
  10. [10]  Zhang, Y., Wang, S., Phillips, P., et al. (2014), Binary PSO with mutation operator for feature selection using decision tree applied to spam detection, Knowledge-Based Systems, 64, 22-31.
  11. [11]  Chen, W. and Zhang, K. (2019), A classifier learning method based on tree reinforced naive bayes, Journal of Electronics & Information Technology, 41(08), 2001-2008.
  12. [12]  Matthijs, J.W. and Bunga, C.P. (2016), Kappa coefficients for circular classifications, Journal of Classification, 33(3), 507-522.
  13. [13]  Xu, S.L. and Wang, J.H. (2016), Data stream classification algorithm based on kappa coefficient, Computer Science, 43(12), 173-178.
  14. [14]  Cheng, G.J., Zhang, W., and Wei, Y.J. (2019), Application of data mining in breast cancer recurrence prediction, Intelligence Computer And Applications, 9(2), 96-99.