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On Combining Neuro--Fuzzy Architectures with the Rough Set Theory to Solve Classification Problems with Incomplete Data

Type of Publication: Article
Year: 2008
  • R. K. Nowicki
Journal: IEEE Trans. on Knowledge and Data Engineering
Volume: 20
Number: 9
Pages: 1239-1253
Month: September
ISSN: 1041-4347
This paper presents a new approach to fuzzy classification in the case of missing features. The rough set theory is incorporated into neuro--fuzzy structures and the rough--neurofuzzy classifier is derived. The architecture of the classifier is determined by the MICOG (modified indexed center of gravity) defuzzification method. The structure of the classifier is presented in a general form which includes both the Mamdani approach and the logical approach - based on the genuine fuzzy aplications. A theorem, which allows to determine the structures of a roughneuro-fuzzy classifiers based on the MICOG defuzzification, is given and proven. Specific rough-neuro-fuzzy structures based on the Larsen rule, the Reichenbach and the Kleene-Dienes implications are given in details. In the experiments it is shown that the classifier with the Dubois-Prade fuzzy implication is characterized by the best performance in the case of missing features

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