Forging New Frontiers: Fuzzy Pioneers II pp 175-197

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

Fuzzy-Neuro Systems for Local and Personalized Modelling

  • Nikola Kasabov
  • Qun Song
  • Tian Min Ma

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.

Keywords

Fuzzy-neuro systems Local modelling Transductive reasoning Personalised modelling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nikola Kasabov
  • Qun Song
  • Tian Min Ma

There are no affiliations available

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