Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification

Fuzzy cognitive maps have gained considerable research interest and widely used to analyze complex systems and making decisions. Recently they have been found large applicability in diverse domains for decision support and classification tasks. A new learning paradigm for FCMs is proposed in this research work, inheriting the main aspects of ensemble based learning approaches, such as bagging and boosting. FCM ensemble learning is an approach where the model is trained using non linear Hebbian learning (NHL) algorithm and further its performance is enhanced using ensemble techniques. This work is inspired from the neural networks ensembles and it is used to learn the FCMs ensembles produced by the already known and efficient data driven NHL algorithm. The new proposed approach of FCM ensembles is applied to identification of Autism and the results are compared with those produced by data driven NHL algorithm alone for FCM training. Experimental results demonstrate that the proposed FCM ensemble algorithm works better than the NHL-based approach alone with respect to accuracy for learning FCM.

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► A new learning approach for FCMs based on ensemble learning (ENFCM-DDNHL) was proposed.
► FCM ensembles were produced by bagging and boosting approaches using the NHL algorithm.
► FCM ensembles learned by the already known data driven NHL as the base learner.
► The new approach of FCM ensembles was applied to identification of Autism.
► The classification performance of ENFCM-DDNHL was significantly increased.