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UID:

Format: 1 Online-Ressource (XIII, 219 p. 57 illus., 55 illus. in color)

Edition: 1st ed. 2024

ISBN: 9783031489631

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-48962-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-48964-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-48965-5

Language: English

DOI: 10.1007/978-3-031-48963-1

URL: Volltext  (URL des Erstveröffentlichers)

URL: lizenzpflichtig

UID:

Format: 1 online resource (228 pages)

Edition: 1st ed.

ISBN: 3-031-48963-2

Note: Intro -- Foreword -- Contents -- Contributors -- Acronyms -- 1 Defining and Using Fuzzy Cognitive Mapping -- 1.1 Introduction -- 1.2 Three Equivalent Definitions -- 1.2.1 FCMs as Mental Models -- 1.2.2 FCMs as Mathematical Objects -- 1.2.3 FCMs as Simulation Tools -- 1.3 A Typology of Uses -- 1.3.1 FCMs as Expert Systems -- 1.3.2 FCMs in Collective Intelligence -- 1.3.3 FCMs as Boundary Objects to Support Learning -- 1.3.4 FCMs as Prediction Models -- References -- 2 Creating an FCM with Participants in an Interview or Workshop Setting -- 2.1 Decision Factors -- 2.1.1 Individual Versus Group Modeling -- 2.1.2 Facilitator Versus Participant Mapping -- 2.1.3 Hand-Drawn Models Versus Modeling Software -- 2.1.4 Pre-defined, Open-Ended Concepts or Hybrid Approach -- 2.2 Data Collection -- 2.2.1 Creating a Parsimonious Model and Weighting Connections -- 2.2.2 Participant Recruitment -- 2.2.3 Facilitation Considerations -- 2.3 Conclusions -- References -- 3 Principles of Simulations with FCMs -- 3.1 Introduction: Revisiting the Reasoning Mechanism -- 3.2 Activation Functions -- 3.3 Convergence: A Mathematical Perspective -- 3.4 Convergence: A Simulation Approach -- 3.5 A Detailed Example in Python -- 3.6 Exercises -- References -- 4 Hybrid Simulations -- 4.1 Introduction -- 4.2 Rationale for a Hybrid ABM/FCM Simulation -- 4.3 Main Steps to Design a Hybrid ABM/FCM Simulation -- 4.4 Example of Study Design and Python Implementation -- 4.5 Scaling-Up Simulations Using Parallelism -- 4.6 Exercises -- References -- 5 Analysis of Fuzzy Cognitive Maps -- 5.1 Why Analyze Fuzzy Cognitive Maps? -- 5.2 What Are the Important Concepts? -- 5.2.1 Transmitter, Receiver, and Ordinary Concepts -- 5.2.2 Centrality Measures -- 5.3 Validating the Facilitation Process -- 5.3.1 Number of Concepts and Relationships -- 5.3.2 Receiver-Transmitter Ratio. , 5.3.3 Metrics Based on Shortest Paths -- 5.3.4 Clustering Coefficient -- 5.3.5 Density -- 5.3.6 Feedback Loops -- 5.4 Conclusion -- 5.5 Exercises -- References -- 6 Extensions of Fuzzy Cognitive Maps -- 6.1 Why Do We Extend Fuzzy Cognitive Maps? -- 6.2 Interval-Valued Fuzzy Cognitive Maps -- 6.2.1 Example Interval-Valued Fuzzy Cognitive Map Inference -- 6.3 Time-Interval Fuzzy Cognitive Maps -- 6.3.1 Example Time-Interval Fuzzy Cognitive Map Inference -- 6.4 Extended-Fuzzy Cognitive Maps -- 6.4.1 Example Extended-Fuzzy Cognitive Map Inference -- 6.5 Trends and Future of Extensions of Fuzzy Cognitive Maps -- 6.6 Exercises -- References -- 7 Creating FCM Models from Quantitative Data with Evolutionary Algorithms -- 7.1 Introduction -- 7.2 Representing the Genome -- 7.2.1 Transformations Between Vector and Matrix -- 7.2.2 Constraints -- 7.3 Evaluation -- 7.4 Genetic Algorithms -- 7.5 Analysis -- 7.6 CMA-ES -- References -- 8 Advanced Learning Algorithm to Create FCM Models From Quantitative Data -- 8.1 Introduction -- 8.2 Hybrid Fuzzy Cognitive Map Model -- 8.3 Training the Hybrid FCM Model -- 8.4 Optimizing the Hybrid FCM Model -- 8.4.1 Detecting Superfluous Relationships -- 8.4.2 Calibrating the Sigmoid Offset -- 8.4.3 Calibrating the Weights -- 8.5 How to Use These Algorithms in Practice? -- 8.6 Applying the FCM Model to Real-World Data -- 8.6.1 Sensitivity to the Sigmoid Function Parameters -- 8.6.2 Comparison with Other Learning Approaches -- 8.7 Further Readings -- 8.8 Exercises -- References -- 9 Introduction to Fuzzy Cognitive Map-Based Classification -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Notions of Classification and Features -- 9.2.2 Preliminary Processing -- 9.2.3 Performance Metrics -- 9.3 The FCM-Based Classification Model -- 9.3.1 Basic FCM Architecture for Data Classification -- 9.3.2 Genetic Algorithm-Based Optimization. , 9.3.3 How Does the Model Classify New Instances? -- 9.4 Classification Toy Case Study -- 9.4.1 Data Description -- 9.4.2 Classifier Implementation -- 9.4.3 Classification-Overall Quality -- 9.5 Further Readings -- 9.6 Exercises -- References -- 10 Addressing Accuracy Issues of Fuzzy Cognitive Map-Based Classifiers -- 10.1 Introduction -- 10.2 Long-Term Cognitive Network-Based Classifier -- 10.2.1 Generalizing the Traditional FCM Formalism -- 10.2.2 Recurrence-Aware Decision Model -- 10.2.3 Learning Algorithm for LTCN-Based Classifiers -- 10.3 Model-Dependent Feature Importance Measure -- 10.4 How to Use the LTCN-Based Classifier in Practice? -- 10.5 Empirical Evaluation of the LTCN Classifier -- 10.5.1 Pattern Classification Datasets -- 10.5.2 Does the LTCN Classifier Outperform the FCM Classifier? -- 10.5.3 Hyperparameter Sensitivity Analysis -- 10.5.4 Comparison of LTCN with State-of-the-Art Classifiers -- 10.6 Illustrative Case Study: Phishing Dataset -- 10.7 Further Readings -- 10.8 Exercises -- References -- Index.

Additional Edition: Print version: Giabbanelli, Philippe J. Fuzzy Cognitive Maps Cham : Springer International Publishing AG,c2024 ISBN 9783031489624

Language: English

UID:

Format: 1 online resource (228 pages)

Edition: 1st ed. 2024.

ISBN: 3-031-48963-2

Content: 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.

Note: 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. .

Additional Edition: Print version: Giabbanelli, Philippe J. Fuzzy Cognitive Maps Cham : Springer International Publishing AG,c2024 ISBN 9783031489624

Language: English

DOI: 10.1007/978-3-031-48963-1

UID:

Format: XIII, 219 p. 57 illus., 55 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031489631

Content: 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.

Note: 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. .

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031489624

Additional Edition: Printed edition: ISBN 9783031489648

Additional Edition: Printed edition: ISBN 9783031489655

Language: English

DOI: 10.1007/978-3-031-48963-1

URL: https://doi.org/10.1007/978-3-031-48963-1