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2010 | 20 | 1 | 55-67

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On classification with missing data using rough-neuro-fuzzy systems

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.








Opis fizyczny




  • Institute of Information Technology, Academy of Management (SWSPiZ), ul. Sienkiewicza 9, 90-113 Łódź, Poland
  • Department of Computer Engineering, Częstochowa University of Technology, ul. Armii Krajowej 36, 42-200 Częstochowa, Poland


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