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

Robert K. Nowicki

International Journal of Applied Mathematics and Computer Science, Tome 20 (2010), p. 55-67 / Harvested from The Polish Digital Mathematics Library

Résumé

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.

Détail

Publié le : 2010-01-01

Zbl 1300.93106

EUDML-ID : urn:eudml:doc:207977

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