Fuzzy Cognitive Maps: Best Practices and Modern Methods (Hardcover, 2024)

· 제목 : Fuzzy Cognitive Maps: Best Practices and Modern Methods (Hardcover, 2024) 
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9783031489624
· 쪽수 : 219쪽
· 출판일 : 2024-01-30

Fuzzy Cognitive Maps: Best Practices and Modern Methods?

Philippe J. Giabbanelli and Gonzalo Napoles

1 Defining and Using Fuzzy Cognitive Mapping?

Philippe J. Giabbanelli, C.B. Knox, Kelsi Furman, Antonie Jetter and

Steven Gray

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?

Exercises?

References?

2 Creating an FCM with participants in an interview or workshop setting?

C.B. Knox, Kelsi Furman, Antonie Jetter, Steven Gray and Philippe J.Giabbanelli

2.1 Decision Factors?

2.1.1 Individual vs Group Modeling?

2.1.2 Facilitator vs. Participant Mapping?

2.1.3 Hand-Drawn Models vs 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?

Exercises?

References

3 Principles of simulations with FCMs?

Gonzalo Napoles and Philippe J. Giabbanelli

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?

Philippe J. Giabbanelli

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?

Ryan Schuerkamp and Philippe J. Giabbanelli

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?

Ryan Schuerkamp and Philippe J. Giabbanelli

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?

David Bernard and Philippe J. Giabbanelli

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?

Exercises?

References?

8 Advanced learning algorithm to create FCM models from quantitative data?

Agnieszka Jastrzebska and Gonzalo Napoles

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?

Agnieszka Jastrzebska and Gonzalo Napoles

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?

Gonzalo Napoles and Agnieszka Jastrzebska

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?