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Knowledge extraction using a genetic fuzzy rule-based system with increased interpretability

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2 Author(s)
Ishibashi, R. ; Div. of Electron. Eng., Inst. Tecnol. de Aeronaut., São José dos Campos, Brazil ; Nascimento, C.L.

In this paper a fuzzy rule-based system is trained to perform a classification task using a genetic algorithm and a fitness function that simultaneously considers the accuracy of the model and its interpretability. Initially a decision tree is created using any tree induction algorithm such as CART, ID3 or C4.5. This tree is then used to generate a fuzzy rule-based system. The parameters of the membership functions are adjusted by the genetic algorithm. As a case study, the proposed method is applied to an appendicitis dataset with 106 instances (input-output pairs), 7 normalized real-valued inputs and 1 binary output.

Published in:

Applied Machine Intelligence and Informatics (SAMI), 2012 IEEE 10th International Symposium on

Date of Conference:

26-28 Jan. 2012