Fuzzy Cognitive Maps designing through large dataset and experts' knowledge balancing

This paper presents an extension of a hybrid method for modelling Fuzzy Cognitive Maps (FCMs), which combines human expert knowledge with existing recorded information and historical data. The proposed approach at first step extracts information from any type of dataset, extracts useful information and transforms it into knowledge in a form suitable to stracture a FCM. The proposed approach is still dependent on experts' knowledge but it support them by providing the information and supportive data inferred from the dataset. Moreover, the approach highlights, through a supportive numerical example, that experts' knowledge and information from the dataset cannot be considered separately, since their combined use compensates possible errors in knowledge. In particular, it can be used to model a complex Decision Support Systems, and it also allows to identify the accuracy of the considered models.