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Design Methodology for Rough Neuro-Fuzzy Classification with Missing Data

Type of Publication: In Proceedings Keywords: Cognition;Electronic mail;Fuzzy sets;Fuzzy systems;Neural networks;Zirconium
Year: 2015
Book title: Computational Intelligence, 2015 IEEE Symposium Series on
Pages: 1650-1657
Month: December
One of important methods designed to classify objects with missing feature values are rough neuro-fuzzy classifiers (RNFC). Similarly to neuro-fuzzy systems, they are specific network structures, which can be trained by optimization methods based on gradient descent. However, to the best of our knowledge, there are no publications concerning such way of RNFC designing. In the paper the problems with gradient learning of RNFC are denoted and the suitable solutions are proposed. The influence of missing values level on the learning process and classification quality is examined. The RNFC is compared with the k-NN classifier which is adapted to missing values problem by a "wide imputation" method. All experiments use 10-fold cross validation.

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