Bagged nonlinear hebbian learning algorithm for fuzzy cognitive maps working on classification tasks

  • Authors:

  • Elpiniki I. Papageorgiou;Panagiotis Oikonomou;Arthi Kannappan

  • Affiliations:

  • Informatics & Computer Technology Department, Technological Educational Institute of Lamia, Lamia, Greece;Computer and Communication Engineering Dept, University of Thessaly, Volos, Greece;RVS College of Computer Applications, Coimbatore, Tamilnadu, India

  • Venue:
  • SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
  • Year:
  • 2012

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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.