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Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data

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  • Shang-Ming Zhou
  • Ronan A Lyons
  • Sinead Brophy
  • Mike B Gravenor

Abstract

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

Suggested Citation

  • Shang-Ming Zhou & Ronan A Lyons & Sinead Brophy & Mike B Gravenor, 2012. "Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
  • Handle: RePEc:plo:pone00:0051468
    DOI: 10.1371/journal.pone.0051468
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    References listed on IDEAS

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    Cited by:

    1. Bimba, Andrew Thomas & Idris, Norisma & Al-Hunaiyyan, Ahmed & Mahmud, Rohana Binti & Abdelaziz, Ahmed & Khan, Suleman & Chang, Victor, 2016. "Towards knowledge modeling and manipulation technologies: A survey," International Journal of Information Management, Elsevier, vol. 36(6), pages 857-871.
    2. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
    3. Shang-Ming Zhou & Ronan A Lyons & Owen G Bodger & Ann John & Huw Brunt & Kerina Jones & Mike B Gravenor & Sinead Brophy, 2014. "Local Modelling Techniques for Assessing Micro-Level Impacts of Risk Factors in Complex Data: Understanding Health and Socioeconomic Inequalities in Childhood Educational Attainments," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-14, November.
    4. Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.

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