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Bagged Nonlinear Hebbian Learning Algorithm for Fuzzy Cognitive Maps Working on Classification Tasks
Elpiniki I. Papageorgiou1, Panagiotis Oikonomou2, and Arthi Kannappan3
1Technological Educational Institute of Lamia, Informatics & Computer Technology Department, 3rd km Old National Road Lamia-Arthens, 35100, Lamia, Greece
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http://users.teilam.gr/~epapageorgiou
2University of Thessaly, Computer and Communication Engineering Dept, 38221, Volos, Greece
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3RVS College of Computer Applications, Coimbatore 641402, Tamilnadu, India
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Abstract. Learning 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.
LNAI 7297, p. 157 ff.
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© Springer-Verlag Berlin Heidelberg 2012