ON CLASSIFICATION WITH MISSING DATA USING ROUGH-NEURO-FUZZY SYSTEMS
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 rough-neuro-fuzzy classifier working in the case of missing features are described.
classification missing data fuzzy sets rough sets neuro-fuzzy architectures