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Bayes Classifier Chain Based on SVM for Traditional Chinese Medical Prescription Generation

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12317)

Abstract

Traditional Chinese Medicine (TCM) plays an important role in the comprehensive treatment of lung cancer. However the quality of the prescriptions from TCM doctors depends on the doctor’s personal experience, which leads to the TCM prescriptions are the lack of standardization. We apply the original clinical TCM prescriptions data to train a standardized prescription generating model for TCM therapy. Our model adopts the Bayes Classifier Chain (BCC) algorithm to solve the label correlation problem, whose basic classifier is cost-sensitive SVM targeted to the class imbalance of the label. The results of experiments on the prescription dataset demonstrated the effectiveness and practicability of the proposed model for a prescription generation.

Keywords

  • Multi-label classification
  • Bayes classifier chain
  • Cost sensitive SVM
  • TCM

This work is supported by the National Science Foundation of China (No. 61672161).

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Notes

  1. 1.

    https://www.csie.ntu.edu.tw/cjlin/libsvm/.

  2. 2.

    https://github.com/xbybshd/TCM-prescription-dataset.

References

  1. Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_7

    CrossRef  Google Scholar 

  2. Bach, F.R., Heckerman, D., Horvitz, E.: Considering cost asymmetry in learning classifiers. J. Mach. Learn. Res. 7(Aug), 1713–1741 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    CrossRef  Google Scholar 

  4. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    CrossRef  Google Scholar 

  5. Cheng, W., Hüllermeier, E., Dembczynski, K.J.: Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 279–286 (2010)

    Google Scholar 

  6. Kubat, M., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: Icml, vol. 97, pp. 179–186. Nashville, USA (1997)

    Google Scholar 

  7. Li, W., Yang, Z., Sun, X.: Exploration on generating traditional Chinese medicine prescription from symptoms with an end-to-end method. arXiv preprint (2018). arXiv:1801.09030

  8. Liu, R., et al.: Chinese herbal decoction based on syndrome differentiation as maintenance therapy in patients with extensive-stage small-cell lung cancer: an exploratory and small prospective cohort study. Evid. Based Complement. Altern. Med. 2015 (2015)

    Google Scholar 

  9. Ma, J., Wang, Z.: Discovering syndrome regularities in traditional Chinese medicine clinical by topic model. 3PGCIC 2016. LNDECT, vol. 1, pp. 157–162. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49109-7_15

    CrossRef  Google Scholar 

  10. Masnadi-Shirazi, H., Vasconcelos, N., Iranmehr, A.: Cost-sensitive support vector machines. arXiv preprint (2012). arXiv:1212.0975

  11. Read, J., Martino, L., Olmos, P.M., Luengo, D.: Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recogn. 48(6), 2096–2109 (2015)

    CrossRef  Google Scholar 

  12. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)

    MathSciNet  CrossRef  Google Scholar 

  13. Ruan, C., Wang, Y., Zhang, Y., Yang, Y.: Exploring regularity in traditional Chinese medicine clinical data using heterogeneous weighted networks embedding. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 310–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18590-9_35

    CrossRef  Google Scholar 

  14. Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)

    CrossRef  Google Scholar 

  15. Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA Cancer J. Clin. 65(2), 87–108 (2015)

    CrossRef  Google Scholar 

  16. Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74958-5_38

    CrossRef  Google Scholar 

  17. Wu, G., Chang, E.Y.: Adaptive feature-space conformal transformation for imbalanced-data learning. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 816–823 (2003)

    Google Scholar 

  18. Xu, Q., Tang, W., Teng, F., Peng, W., Zhang, Y., Li, W., Wen, C., Guo, J.: Intelligent syndrome differentiation of traditional chinese medicine by ANN: a case study of chronic obstructive pulmonary disease. IEEE Access 7, 76167–76175 (2019)

    CrossRef  Google Scholar 

  19. Yao, L., Zhang, Y., Wei, B., Zhang, W., Jin, Z.: A topic modeling approach for traditional chinese medicine prescriptions. IEEE Trans. Knowl. Data Eng. 30(6), 1007–1021 (2018)

    CrossRef  Google Scholar 

  20. Zhang, T., et al.: Statistical behavior and consistency of classification methods based on convex risk minimization. Ann. Stat. 32(1), 56–85 (2004)

    MathSciNet  CrossRef  Google Scholar 

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Correspondence to Yanchun Zhang .

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Pei, C., Ruan, C., Zhang, Y., Yang, Y. (2020). Bayes Classifier Chain Based on SVM for Traditional Chinese Medical Prescription Generation. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_55

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  • DOI: https://doi.org/10.1007/978-3-030-60259-8_55

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