Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization

@article{Evsukoff2009DesignOI,
  title={Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization},
  author={Alexandre Evsukoff and Sylvie Galichet and Beatriz S. L. P. de Lima and Nelson F. F. Ebecken},
  journal={Fuzzy Sets and Systems},
  year={2009},
  volume={160},
  pages={857-881}
}
This paper presents a design method for fuzzy rule-based systems that performs data modeling consistently according to the symbolic relations expressed by the rules. The focus of the model is the interpretability of the rules and the model’s accuracy, such that it can be used as tool for data understanding. The number of rules is defined by the eigenstructure analysis of the similarity matrix, which is computed from data. The rule induction algorithm runs a clustering algorithm on the dataset… CONTINUE READING
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