By Topic

An Extension to Fuzzy Cognitive Maps for Classification and Prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Hengjie J. Song ; School of Computer Engineering , Nanyang Technical University, Singapore ; Chunyan Y. Miao ; Roel Wuyts ; Zhiqi Q. Shen
more authors

Fuzzy cognitive maps (FCMs), as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the dynamic behavior of the investigated systems. However, due to defects in expression and architecture, the traditional FCMs and most of their relevant extensions are not applicable to classification problems. To solve this problem, this paper presents an approach that directly extends the model by translating the reasoning mechanism of traditional FCMs to a set of fuzzy IF-THEN rules. Moreover, the proposed approach fully considers the contribution of the inputs to the activation of the fuzzy rules and quantifies the causalities using mutual subsethood, which works in conjunction with volume defuzziflcation in a gradient descent-learning framework. In this manner, our approach enhances the capability of the conventional FCMs to automatically identify membership functions and quantify causalities. Despite the increase in the number of tunable parameters, experimental results show that the proposed approach efficiently extends the application of the traditional FCMs into classification problems, while keeping the ability for prediction and approximation.

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:19 ,  Issue: 1 )