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

  • Authors:
  • Robert Nowicki

  • Affiliations:
  • -

  • Venue:
  • International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
  • Year:
  • 2010

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Abstract

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.