Bagged nonlinear Hebbian learning algorithm for fuzzy cognitive maps working on classification tasks
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. © 2012 Springer-Verlag .
Showing items related by title, author, creator and subject.
Hartonas, C.; Gana, E. (2008)With the development of the web and the intense research for a semantic web, the need arises for standardized and rigorous semantic specification both of Services on the semantic web and of the objects and processes that ...
Kaldi, S.; Filippatou, D.; Govaris, C. (2011)This study focuses upon the effectiveness of project-based learning on primary school pupils regarding their content knowledge and attitudes towards self-efficacy, task value, group work, teaching methods applied and peers ...