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Fuzzy cognitive maps = best practices and modern methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fuzzy cognitive maps/ edited by Philippe J. Giabbanelli, Gonzalo Napoles.
Reminder of title:	best practices and modern methods /
other author:	Giabbanelli, Philippe J.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xiii, 219 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Fuzzy algorithms. -
Online resource:	https://doi.org/10.1007/978-3-031-48963-1
ISBN:	9783031489631

Fuzzy cognitive maps = best practices and modern methods /
Fuzzy cognitive mapsbest practices and modern methods /[electronic resource] :edited by Philippe J. Giabbanelli, Gonzalo Napoles. - Cham :Springer Nature Switzerland :2024. - xiii, 219 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783031489631

Standard No.: 10.1007/978-3-031-48963-1doiSubjects--Topical Terms:

895969
Fuzzy algorithms.

LC Class. No.: QA248.5

Dewey Class. No.: 003.7

Fuzzy cognitive maps = best practices and modern methods /
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505 0 $a 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.
520 $a 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.
650 0 $a Fuzzy algorithms. $3 895969
650 0 $a Computational intelligence. $3 595739
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650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Knowledge Management. $3 900242
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
700 1 $a Giabbanelli, Philippe J. $3 3321516
700 1 $a Napoles, Gonzalo. $3 3713724
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-48963-1
950 $a Mathematics and Statistics (SpringerNature-11649)