A multi-stage learning-based fuzzy cognitive maps for tobacco use
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ORIGINAL ARTICLE
A multi-stage learning-based fuzzy cognitive maps for tobacco use Pınar Kocabey Çiftçi1 • Zeynep Didem Unutmaz Durmus¸og˘lu1 Received: 4 September 2019 / Accepted: 14 March 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Fuzzy cognitive map (FCM) is an important approach for modeling the behavior of dynamic systems. FCM’s ability to represent casual relationships between the concepts (factors, attributes, etc.) has attracted the interest of researchers from different disciplines. The construction process of FCMs is mostly initialized with expert knowledge because FCMs can conveniently incorporate available information and expertise in the determination of vital parameters and relations of the system. However, their higher dependence on expert knowledge may significantly influence the reliability of the model due to the increase in subjectivity. In order to avoid weaknesses depending on expert knowledge, learning algorithms that search for the appropriate relationships between the concepts have been used with FCM studies. In this paper, a FCM analysis was performed for tobacco use to understand the cause–effect relationships between demographic characteristics of people (such as gender, age range, and residence type) and likelihood to tobacco use. In order to reduce the impact of external interventions (from experts), a multi-stage learning procedure was applied by integrating two different learning algorithms (nonlinear Hebbian learning algorithm and extended Great Deluge algorithm). The results showed that the multi-stage learning procedure increased the accuracy of the model and provided more reliable maps for the studied system. They also proved that the multi-stage learning procedures can help to reduce the dependency to expert knowledge and improve the robustness of the study. Keywords Fuzzy cognitive map Nonlinear Hebbian learning algorithm Extended Great Deluge algorithm
1 Introduction Predicting or modeling the behavior of a real-life system is a challenging process. Remembering the definitions of the ‘‘system’’ can be helpful to understand the main reason behind why it becomes a challenge for researchers. The term ‘‘system’’ was defined numerous times over decades by researchers from different disciplines. Although the debates on the scope of the definitions still go on, presenting the basic ones is sufficient for our purpose in the content of this study. The Oxford Dictionaries defines the ‘‘system’’ as [1]:‘‘a set of things working together as a part of a mechanism or an interconnecting network; a complex
& Pınar Kocabey C¸iftc¸i [email protected] Zeynep Didem Unutmaz Durmus¸ og˘lu [email protected] 1
Department of Industrial Engineering, Gaziantep University, 27310 S¸ ehitkamil, Gaziantep, Turkey
whole’’ while the Cambridge Dictionary states as [2]: ‘‘a set of connected things or devices that operate together’’. In addition to these, Miller defines the ‘‘system’’ as [3]: ‘‘a set of interactin
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