Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Evidence-Based Complementary and Alternative Medicine, enter your email address in the box below.

Evidence-Based Complementary and Alternative Medicine
Volume 2012, Article ID 135387, 9 pages
http://dx.doi.org/10.1155/2012/135387
Research Article

Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

Guo-Ping Liu,1 Jian-Jun Yan,2 Yi-Qin Wang,1 Jing-Jing Fu,1 Zhao-Xia Xu,1 Rui Guo,1 and Peng Qian1

1Laboratory of Information Access and Synthesis of TCM Four Diagnosis, Basic Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
2Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China

Received 13 January 2012; Accepted 22 March 2012

Academic Editor: Shi-Bing Su

Copyright © 2012 Guo-Ping Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. M. Zheng, L. S. Hu, Y. L. Li, and R. Biao, “Screening methods of traditional Chinese medicine,” Chinese Journal of Basic Medicine in Traditional Chinese Medicine, vol. 14, pp. 304–305, 2008. View at Google Scholar
  2. G. C. Li, C. T. Li, L. P. Huang et al., “An investigation into regularity of syndrome classification for chronic atrophic gastritis based on structural equation model,” Journal of Nanjing University of Traditional Chinese Medicine, vol. 22, pp. 217–220, 2006. View at Google Scholar
  3. Y. J. Bai, Q. G. Meng, H. B. Shen et al., “Study on non-linear modelling of TCM syndrome of DN based on improved BP neural network model,” Journal of Beijing University of Traditional Chinese Medicine, vol. 31, pp. 308–311, 2008. View at Google Scholar
  4. H. L. Gao, Y. L. Wu, J. Chen et al., “The complex system based on entropy method to partition the syndrome diagnosis standard research of application,” China Journal of Traditional Chinese Medicine and Pharmacy, vol. 23, pp. 307–309, 2008. View at Google Scholar
  5. G. P. Liu, G. Z. Li, Y. L. Wang, and Y. Q. Wang, “Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning,” BMC Complementary and Alternative Medicine, vol. 10, article 37, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Su, L. Wang, and H. Zhang, “Posthepatitic cirrhosis of TCM syndrome and disease information similarity analysis,” Chinese Journal of Integrated Traditional and Western Medicine, vol. 29, pp. 398–402, 2009. View at Google Scholar
  7. G. P. Liu, Y. Q. Wang, Z. X. Xu et al., “Based on the structure of traditional Chinese medicine of spleen hidden information objectified by analysis,” Journal of Traditional Chinese Medicine, vol. 52, pp. 1372–1375, 2011. View at Google Scholar
  8. G. P. Liu, Y. L. Wang, Y. Q. Wang et al., “Coronary Heart Disease of TCM syndrome association,” Journal of Liaoning University of Traditional Chinese Medicine, vol. 38, pp. 821–824, 2011. View at Google Scholar
  9. G. P. Liu, Y. Q. Wang, F. F. Li et al., “Association analysis and distribution of chronic gastritis syndromes based on associated density,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW '10), pp. 790–794, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Chinese Medical Association Digestive Diseases Branch, “Consensus of national seminar on chronic gastritis,” Chinese Journal of Digestive Endoscopy, vol. 24, pp. 58–63, 2007. View at Google Scholar
  11. X. Y. Zheng, Chinese Herbal Medicine Clinical Research Guiding Principles (for Trial Implementation), China Medical Science and Technology Press, Beijing, China, 2004.
  12. State Bureau of Technical Supervision, National Standards of Peoples Republic of China: Syndrome Part of TCM Clinical Diagnosis and Treatment, Standards Press of China, Beijing, China, 1997.
  13. G. P. Liu, Y. Q. Wang, Y. Dong et al., “Development and evaluation of Scale for heart system Inquiry of TCM,” Journal of Chinese Integrative Medicine, vol. 7, pp. 1222–1225, 2009. View at Google Scholar
  14. W. C. Kenneth and H. Patrick, “Word association norms, mutual information, and lexicography,” in Proceedings of the 27th Annual Meeting on Association for Computational Linguistics (ACL '89), vol. 27, pp. 76–83, Vancouver, Canada, 1989.
  15. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Mitchell, Machine Learning, McCraw Hill, 1996.
  17. F. Fleuret, “Fast binary feature selection with conditional mutual information,” Journal of Machine Learning Research, vol. 5, pp. 1531–1555, 2004. View at Google Scholar
  18. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of Max-Dependency, Max-Relevance, and Min-Redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. R. E. Schapire and Y. Singer, “BoosTexter: a boosting-based system for text categorization,” Machine Learning, vol. 39, no. 2, pp. 135–168, 2000. View at Google Scholar · View at Scopus
  20. M. L. Zhang and Z. H. Zhou, “ML-KNN: a lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no. 7, pp. 2038–2048, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Y. You, Y. Chen, and G. Z. Li, “Disequilibrium problem of the new feature selection algorithm: Im-IG,” Shandong University Journal, vol. 40, pp. 123–128, 2010. View at Google Scholar
  22. H. Shao, G. Z. Li, and G. P. Liu, “Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine,” Science China Information Sciences, vol. 41, pp. 1372–1387, 2011. View at Google Scholar
  23. Y. J. Xu, TCM tongue pulse diagnosis system and tongue diagnosis digital pulse, Ph.D. thesis, Beijing University of Chinese Medicine.