COW: A Co-evolving Memetic Wrapper for Herb-Herb Interaction Analysis in TCM Informatics

  • Dion Detterer
  • Paul Kwan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)


Traditional Chinese Medicine (TCM) relies heavily on interactions between herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of herb-herb interactions by pure human analysis is impractical, with computeraided analysis computationally expensive. Thus feature selection is crucial as a pre-processing step prior to herb-herb interaction analysis. In accord with this goal, a new feature selection algorithm known as a Co-evolving Memetic Wrapper (COW) is proposed: COW takes advantage of recent developments in genetic algorithms (GAs) and memetic algorithms (MAs), evolving appropriate feature subsets for a given domain. As part of preliminary research, COW is demonstrated to be effective in selecting herbs in the TCM insomnia datatset. Finally, possible future applications of COW are examined, both within TCM research and in broader data mining contexts.


Traditional Chinese Medicine memetic algorithm wrapper feature selection data mining 


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  1. 1.
    Baker, J.E.: Adaptive Selection Methods for Genetic Algorithms. In: Proc. Int’l Conf. Genetic Algorithm and Their Applications, pp. 101–111. Lawrence Erlbaum Associates, New Jersey (1985)Google Scholar
  2. 2.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The J. of Machine Learning Research, 1157–1182 (2003)Google Scholar
  3. 3.
    Kononenko, I., Šimec, E., Robnik-Šikonja, M.: Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, 39–55 (1997)Google Scholar
  4. 4.
    Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. American J. of Human Genetics, 138–147 (2001)Google Scholar
  5. 5.
    Smith, J.E.: Coevolving memetic algorithms: a review and progress report. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 6–17 (2007)Google Scholar
  6. 6.
    Wing, Y.K.: Herbal treatment of insomnia. Hong Kong Med. J., 392–402 (2001)Google Scholar
  7. 7.
    Zhou, X., Chen, S., Liu, B., et al.: Development of Traditional Chinese Medicine Clinical Data Warehouse for Medical Knowledge Discovery and Decision Support. Artificial Intelligence in Medicine 48(2-3), 139–152 (2010)CrossRefGoogle Scholar
  8. 8.
    Zhou, X., Poon, J., Kwan, P., Zhang, R., Wang, Y., Poon, S., Liu, B., Sze, D.: Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia. In: Zhang, D. (ed.) ICMB 2010. LNCS, vol. 6165, pp. 258–267. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Zhu, Z., Ong, Y.S., Dash, M.: Wrapper–filter feature selection algorithm using a memetic framework. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 70–76 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dion Detterer
    • 1
  • Paul Kwan
    • 1
  1. 1.School of Science and TechnologyUniversity of New EnglandArmidaleAustralia

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