MICOG defuzzification rough-neuro-fuzzy system ensemble

Most methods constituting the soft computing concept can not handle data with missing or unknown feature values. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. In the paper we incorporate rough set theory to neuro-fuzzy system of very specific type. This results in learning systems which can work when the set number of available feature values is changing. To achieve better accuracy learning systems can be combined into larger ensembles. In the paper the AdaBoost metalearning is used to create an ensemble of learning systems. The rough-neuro-fuzzy systems use knowledge comprised in the form of fuzzy rules to perform classification. Simulations on a well-known benchmark give legitimacy to use the method in real world applications.