04691nam a22005535i 4500 978-3-031-48963-1 DE-He213 20251030111406.0 cr nn 008mamaa 240129s2024 sz | s |||| 0|eng d 9783031489631 978-3-031-48963-1 10.1007/978-3-031-48963-1 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Fuzzy Cognitive Maps [electronic resource] : Best Practices and Modern Methods / edited by Philippe J. Giabbanelli, Gonzalo Nápoles. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIII, 219 p. 57 illus., 55 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 1. Fuzzy Cognitive Maps: Best Practices and Modern Methods -- 2. Creating an FCM with participants in an interview or workshop setting -- 3. Principles of simulations with FCMs -- 4. Hybrid Simulations -- 5. Analysis of Fuzzy Cognitive Maps -- 6. Extensions of Fuzzy Cognitive Maps -- 7. Creating FCM models from quantitative data with evolutionary algorithms -- 8. Advanced learning algorithm to create FCM models from quantitative data -- 9. Introduction to Fuzzy Cognitive Map-based classification -- 10. Addressing accuracy issues of Fuzzy Cognitive Map-based classifiers -- Index. . This book starts with the rationale for creating an FCM by contrast to other techniques for participatory modeling, as this rationale is a key element to justify the adoption of techniques in a research paper. Fuzzy cognitive mapping is an active research field with over 20,000 publications devoted to externalizing the qualitative perspectives or “mental models” of individuals and groups. Since the emergence of fuzzy cognitive maps (FCMs) back in the 80s, new algorithms have been developed to reduce bias, facilitate the externalization process, or efficiently utilize quantitative data via machine learning. It covers the development of an FCM with participants through a traditional in-person setting, drawing from the experience of practitioners and highlighting solutions to commonly encountered challenges. The book continues with introducing principles of simulations with FCMs as a tool to perform what-if scenario analysis, while extending those principles to more elaborated simulation scenarios where FCMs and agent-based modeling are combined. Once an FCM model is obtained, the book then details the analytical tools available for practitioners (e.g., to identify the most important factors) and provides examples to aid in the interpretation of results. The discussion concerning relevant extensions is equally pertinent, which are devoted to increasing the expressiveness of the FCM formalism in problems involving uncertainty. The last four chapters focus on building FCM models from historical data. These models are typically needed when facing multi-output prediction or pattern classification problems. In that regard, the book smoothly guides the reader from simple approaches to more elaborated algorithms, symbolizing the noticeable progress of this field in the last 35 years. Problems, recent references, and functional codes are included in each chapter to provide practice and support further learning from practitioners and researchers. Machine learning. 55059 Computational intelligence. 64052 Knowledge management. Neural networks (Computer science) . 64231 Machine Learning. 55059 Computational Intelligence. 64052 Knowledge Management. Mathematical Models of Cognitive Processes and Neural Networks. 65860 Giabbanelli, Philippe J. editor. edt http://id.loc.gov/vocabulary/relators/edt 70084 Nápoles, Gonzalo. editor. edt http://id.loc.gov/vocabulary/relators/edt 70085 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031489624 Printed edition: 9783031489648 Printed edition: 9783031489655 https://doi.org/10.1007/978-3-031-48963-1 ZDB-2-SMA ZDB-2-SXMS 57353 57353