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dc.creatorPapageorgiou, E. I.en
dc.creatorOikonomou, P.en
dc.creatorKannappan, A.en
dc.description.abstractLearning of fuzzy cognitive maps (FCMs) is one of the most useful characteristics which have a high impact on modeling and inference capabilities of them. The learning approaches for FCMs are concentrated on learning the connection matrix, based either on expert intervention and/or on the available historical data. Most learning approaches for FCMs are Hebbian-based and evolutionary-based algorithms. A new learning algorithm for FCMs is proposed in this research work, inheriting the main aspects of the bagging approach which is an ensemble based learning approach. The FCM nonlinear Hebbian learning (NHL) algorithm enhanced by the bagging technique is investigated contributing to an approach where the model is trained using NHL algorithm as a base learner classifier. This work is inspired from the neural networks ensembles and it is used to learn the FCM ensembles produced by the NHL exploiting better classification accuracies. © 2012 Springer-Verlag .en
dc.subjectBagging approachen
dc.subjectBase learnersen
dc.subjectClassification accuracyen
dc.subjectClassification tasksen
dc.subjectConnection matricesen
dc.subjectFuzzy cognitive mapen
dc.subjectHebbian learningen
dc.subjectHebbian learning algorithmen
dc.subjectHigh impacten
dc.subjectHistorical dataen
dc.subjectLearning approachen
dc.subjectArtificial intelligenceen
dc.subjectFuzzy rulesen
dc.subjectFuzzy systemsen
dc.subjectLearning algorithmsen
dc.titleBagged nonlinear Hebbian learning algorithm for fuzzy cognitive maps working on classification tasksen

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