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

Robert Nowicki

doi:10.2478/v10006-010-0004-8

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

International Journal of Applied Mathematics and Computer Science

Volume 20 (2010): Issue 1 (March 2010)

By: Robert Nowicki

Open Access

|Mar 2010

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.

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