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Fuzzy-Neuro Systems for Local and Personalized Modelling

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Forging New Frontiers: Fuzzy Pioneers II

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 218))

Abstract

This chapter presents a review of recently developed fuzzy-neuro models for local and personalised modelling and illustrates them on a real world case study from medical decision support. The local models are based on the principles of evolving connectionist systems, where the data is clustered and for each cluster a separate local model is developed and represented as a fuzzy rule, either of Takagi-Sugeno, or Zadeh-Mamdani types. The personalised modelling techniques are based on transductive reasoning and include a model called TWNFI. It is also illustrated on medical decision support problem where a model for each patient is developed to predict an outcome for this patient and to rank the importance of the clinical variables for them. The local and personalised models are compared with statistical, neural network and fuzzy-neuro global models and show a significant advantage in accuracy and explanation.

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Authors
  1. Nikola Kasabov
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  2. Qun Song
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  3. Tian Min Ma
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Kasabov, N., Song, Q., Ma, T.M. (2008). Fuzzy-Neuro Systems for Local and Personalized Modelling. In: Forging New Frontiers: Fuzzy Pioneers II. Studies in Fuzziness and Soft Computing, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73185-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-73185-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73184-9

  • Online ISBN: 978-3-540-73185-6

  • eBook Packages: EngineeringEngineering (R0)

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