Explainable AI and Other Applications of Fuzzy Techniques

Name: Explainable AI and Other Applications of Fuzzy Techniques | Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021
Brand: Springer
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Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021

Julia Rayz Victor Raskin Scott Dick Vladik Kreinovich(Herausgeber*in)

Springer (Verlag)

Erschienen am 27. Juli 2021

XII, 506 Seiten

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Inhalt

Intro
Explainability: New Application and New Promise of Fuzzy Techniques
Contents
A Fuzzy Logic Approach for Spacecraft Landing Site Selection
1 Introduction
2 Image Dataset
3 Methodology
3.1 Image Size Reduction
3.2 Object Grouping
3.3 Object Classification
3.4 Performance Metrics
4 Results
5 Conclusions and Future Work
5.1 Future Work
References
Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions as Interpretable Neural Networks
1 Introduction
2 Preliminaries
2.1 Fuzzy Systems
2.2 Neural Networks
3 Equivalence Between TS Fuzzy Systems with Triangular Membership And Neural Networks with ReLU Activation
3.1 TS Fuzzy Systems Expressed in Terms of ReLU Functions
3.2 TS Fuzzy Systems as Neural Networks with ReLU Activation
3.3 Neural Networks Expressed in Terms of Takagi-Sugeno Fuzzy Systems
4 Conclusions
References
A Study on Constrained Interval Arithmetic
1 Introduction
2 Constrained Interval Arithmetic
2.1 Addition in CIA
2.2 Difference in CIA
2.3 Multiplication in CIA
2.4 Division in CIA
2.5 Additive/Multiplicative Inverse in CIA
3 Distributivity, Expression/component-Wise and Optimization
4 Inclusion Isotonicity
5 Final Remarks
References
Fuzzy Classification of Multi-intent Utterances
1 Introduction
2 Related Work
3 Utterance Level Fuzzy Memberships
3.1 Membership Functions
3.2 Parameter Generation
4 Single Intent to Multi Intent Utterances
5 Fuzzy Membership Aggregation and Defuzzification
6 Experiments
6.1 Setup
6.2 Data
6.3 Training Details
6.4 Results
7 Conclusion
References
How Much for a Set: General Case of Decision Making Under Set-Valued Uncertainty
1 Decision Making Under Set-Valued Uncertainty: Formulation of the Problem
2 What Is Known: Cases of Closed Intervals and Closed Sets
3 Extending the Known Result to General Bounded Sets
4 What if We Do Not Require Additivity?
Reference
A Deep Fuzzy Semi-supervised Approach to Clustering and Fault Diagnosis of Partially Labeled Semiconductor Manufacturing Data
1 Introduction
2 Methodology
2.1 Deep Convolutional Unsupervised Feature Learning
2.2 PCA-Based Semi-supervised Fault Classification
2.3 Fuzzy c-means Clustering and Borderline Case Detection
3 Results
3.1 Case Study Description
3.2 Semi-supervised Classification Results
3.3 Fuzzy c-means Clustering Results
4 Discussion
5 Conclusion
References
Why Fuzzy Techniques in Explainable AI? Which Fuzzy Techniques in Explainable AI?
1 Why Fuzzy Techniques in Explainable AI
2 Which Fuzzy Techniques in Explainable AI
References
Why Are Fuzzy and Stochastic Calculus Different?
1 Introduction
2 Introduction to Stochastic Equations and First Issues with Derivatives
3 Modes of Convergence
4 Convergence in Distribution
5 Is Weakening the Topology a Viable Solution?
6 Comparison with Fuzzy Calculus. Common Problems and Conclusion
References
A Genetic Fuzzy Approach for the Prediction of Heart Failure
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusion
References
COVID-19 Vaccination Priority Evaluation
1 Introduction
2 The Vaccination Priority Degree and Its Use
3 The Vaccination Priority Attribute Tree
4 Vaccination Priority Attribute Criteria
5 Logic Aggregation
6 Examples and Discussion
7 Conclusions
References
Comparing the Explainability and Performance of Reinforcement Learning and Genetic Fuzzy Systems for Safe Satellite Docking
1 Introduction
2 Background
3 Methodology
3.1 Run Time Assurance
3.2 Reinforcement Learning Approach
3.3 Genetic Fuzzy System Approach
3.4 Run Time Assurance Approach
3.5 Reward Function
4 Results
4.1 Training
4.2 Explainability
5 Conclusion
References
A Note on the KM Algorithm for Computing the Variance of an Interval Type-2 Fuzzy Set
1 Introduction and Motivation
2 Basics of Interval Type-2 Fuzzy Numbers
3 The KM Algorithm
3.1 a) KM Algorithm for Computing yl
3.2 b) KM Algorithm for Computing yr
4 Computing the Variance of F2(X)
5 Illustrative Examples
5.1 Trapezoidal IT2FS
5.2 Nonlinear IT2FS
6 Concluding Remarks
References
Estimating Fuzzy Possibility Functions for E-Commerce Decision Support
1 Introduction
2 Methodology
2.1 Data Experiments
2.2 Linguistic Pre-processing
2.3 Feature Extraction and Statistical Methods
2.4 Model Evaluation
3 Soft Business Function
4 Results
5 Final Remarks
References
Machine Learning to Augment the Fusion Process for Data Classification
1 Introduction
2 Dempster-Shafer Fusion
3 Q-Learning
3.1 Q-Learning Fundamentals
3.2 Dempster-Shafer Fusion Calculus Integrated into Q-Learning
4 Experiments
5 Conclusions and Future Research
References
Managing Uncertainty in Crowdsourcing with Interval-Valued Labels
1 Introduction
1.1 Uncertainties in Crowdsourcing
1.2 Previous Results and Motivations of This Study
2 Interval-Valued Labels and Their Properties
2.1 An Interval-Valued Label and Its Properties
2.2 Statistic and Probabilistic Properties of a Set of IVLs on the Same xi
3 Methods for Pre-processing IVLs and Inference Making
3.1 Strategies on Pre-processing Li
3.2 Making an Inference from Li
4 Computational Experiments
4.1 Comparisons of Inferences with MV and Algorithm2 from the Same Li
4.2 Impacts of Biased IVLs on the Distribution of Matching Probability
5 Summary
References
Modeling Fuzzy Cluster Transitions for Topic Tracing
1 Introduction
2 Related Work
3 Modeling Cluster Transition with Fuzzy Logic
3.1 Crisp Cluster Transitions for Topic Tracing
3.2 Fuzzified Cluster Transitions
4 Implementation: Comparing Crisp and Fuzzy Cluster Transitions
5 Conclusion
References
A Natural Formalization of Changing-One's-Mind Leads to Square Root of ``Not'' and to Complex-Valued Fuzzy Logic
1 Formulation of the Problem
2 Analysis of the Problem and the Resulting Derivation of Square Root of ``Not" and of Complex-Valued Fuzzy Logic
References
Each Realistic Continuous Functional Dependence Implies a Relation Between Some Variables: A Theoretical Explanation of a Fuzzy-Related Empirical Phenomenon
1 Formulation of the Problem
2 Formalization of the Problem
3 Main Result
4 Auxiliary Result: Case of m=1
References
Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems
1 Rule-Based Probability Mixtures and XAI
2 Probability Structure of Additive Fuzzy Rule-Based Systems
3 Uniform Convergence of Mixtures of Transformed Fuzzy Systems
4 Conclusions
References
Solutions of Systems of Linear Fuzzy Differential Equations for a Special Class of Fuzzy Processes
1 Introduction
2 Mathematical Background
3 Calculus Theory for Fuzzy Functions Under -derivative
4 Systems of Linear FDEs for S-linearly Correlated Fuzzy Processes
5 Application on Mass-Spring Multiple System
6 Final Remarks
References
Abstractive Representation Modeling for Image Classification
1 Introduction
1.1 Background
1.2 Identified Research
2 Data
2.1 MNIST Digit Dataset
3 Method
3.1 Abstractive Processing
3.2 Abstractive Level Design
3.3 Pattern Frequency Detector
3.4 Classifier
4 Results
4.1 Global Performance Analysis of ARM and CNN
4.2 Robustness Analysis of ARM and CNN
5 Conclusion
5.1 Contributions
5.2 Future Research
References
A-Cross Product for Autocorrelated Fuzzy Processes: The Hutchinson Equation
1 Introduction
2 Fuzzy Set Theory
3 A-cross Product of Linearly Correlated Fuzzy Numbers
4 Fuzzy Differential Equations Under Fréchet Derivative
5 The Hutchinson Equation
5.1 Crisp Logistic Growth Rate
5.2 Fuzzy Logistic Growth Rate
6 Final Considerations
References
Use of T-Norm in an Epidemiological Model for COVID-19
1 Introduction
1.1 Triangular Norm and SIR Model
2 Methodology
3 Application and Results
4 Comments and Discussions
5 Conclusion
References
Fuzzy-Flatness Hybrid Fault-Tolerant Control
1 Introduction
2 Differential Flatness
3 3-Tank Problem
4 Fuzzy-Flatness Hybrid Control
5 Results
6 Conclusion
References
Finding Fuzziness in Neural Network Models of Language Processing
1 Introduction
2 The Concept of Vagueness and the Vagueness of Concepts
2.1 Psychological Significance
2.2 Handling Vagueness Using Fuzzy Interpretations of Linguistic Hedges
3 Pre-trained Language Models
4 Vagueness in Natural Language Inference Models
4.1 Methodology
5 General Discussion and Conclusion
References
Interval Arithmetic: WSM, CI or RDM?
1 Introduction
2 CIA and RDM Arithmetics
2.1 Fuzzy Interval Linear Systems
3 Conclusion
References
Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS-TSK
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusions
References
A New GA-PSO Optimization Methodology with Fuzzy Adaptive Inertial Weight
1 Introduction
2 Proposed Optimization Methodology
2.1 Parents Selection Genetic Operator
2.2 Crossover Genetic Operator
2.3 Mutation Genetic Operator
2.4 Velocity and Position Vector Update
2.5 Fuzzy Adaptive Inertial Weight
3 Computational Results
4 Conclusion
References
Predicting Diabetes Diagnosis with Binary-To-Fuzzy Extrapolations and Weights Tuned via Genetic Algorithm
1 Introduction
1.1 Diabetes Prevalence, Diagnosis, Physical, and Monetary Costs
1.2 Author Exposure
1.3 Disease Prediction Using Intelligent Systems
1.4 Kernels and Fuzzy Equivalence Relations in Fuzzy Logic
2 Diabetes Dataset
2.1 Data Acquisition and Prior Method Comparison
2.2 Clinical Characteristics of Study Subjects
3 Methodology
3.1 Fuzzy Relation Kernels, Optimization, and Prediction
3.2 Genetic Algorithm Parameters
3.3 Additional Metrics
4 Results
4.1 Accuracy of Predictions Using Best Chromosome
4.2 Confusion Matrix and Performance
4.3 Method Comparison
5 Discussion
6 Conclusion
References
Genetic Fuzzy Hand Gesture Classifier
1 Introduction
2 Literature Review
3 Hand Gesture Data Set
4 Methodology
5 Results
5.1 Training
5.2 Testing
5.3 Data Visualization
6 Conclusion and Future Work
References
Toward Explainable AI-Genetic Fuzzy Systems-A Use Case
1 Introduction
1.1 Fuzzy Logic
1.2 Genetic Algorithm
1.3 Aggregate Fuzzy Tree
2 Methodology
2.1 The Breast Cancer Data Set
3 Results
3.1 Test Confusion Matrices
3.2 Train Confusion Matrices
3.3 Analysis of Results: Fixed Input Variable Mutation Method
4 Discussion
5 Summary and Conclusions
References
Uncertainty to Avoid Entrapment: Comparing Internet Stings to Real Victim Conversations
1 Introduction
2 Background
3 Related Work
4 Methodology
4.1 Corpus Composition
4.2 Annotation
5 Preliminary Results
5.1 Friendship Forming
5.2 Relationship Forming
5.3 Exclusivity
5.4 Sexual
5.5 Meeting
5.6 Risk Assessment
6 Conclusion and Future Work
References
FLTRL: A Fuzzy-Logic Transfer Learning Powered Reinforcement Learning Method for Intelligent Online Control in Power Systems
1 Introduction
2 The Problem Settings
3 The Reinforcement-Learning-Based Control Mechanism
4 Fuzzy-Logic-Based Transfer Learning Mechanism
4.1 Transfer Learning Model
4.2 Fuzzy Strategy
5 Simulation Results
5.1 Demand Surge
5.2 Generator Fault Alongside Demand Surge
6 Conclusions
References
Genetic Fuzzy Methodology to Predict Time to Return to Play from Sports-Related Concussion
1 Introduction
2 Methodology
2.1 Dataset Description
2.2 Data Reduction
2.4 Regression Problem: Predicting the RTP Days
2.5 Classification Problem
3 Results
3.1 Evaluating the Trained GFS on the Regression Problem
3.2 Evaluating the Trained GFS on the Classification Problem
4 Conclusions
References
Fuzzy Information Processing Computing Curricula: A Perspective from the First Two-Years in Computing Education
1 Introduction
2 Challenges and Opportunities
3 Computer Science Practices: Learning Outcomes, Alignment, and Adaptation
4 Discussion, Current Efforts, and Future Work
References
What Teachers Can Learn from Machine Learning
1 Introduction
2 First Idea: Take into Account that There Are Very Wrong and Somewhat Wrong Answers
3 Second Idea: Asking Why-Questions, Not Just Checking Where Answers Are Correct
4 Third Idea: Let Us Be Positive
5 Fourth Idea: Making Learning More Robust
6 Fifth Idea: Averaging - This Is Not What You Think
7 What Else?
References
Self-tuned Model-Based Predictive Control Using Evolving Fuzzy Model of a Non-linear Dynamic Process
1 Introduction
2 Evolving Fuzzy Model Identification
2.1 ARX Linear Model
2.2 RLS
2.3 Filtering of Regressors
2.4 Non-linear Model
2.5 Model Excitation for Identification
2.6 Model Merging
2.7 Model Combination
3 Predictive Functional Controller
3.1 PFC Derivation
3.2 PFC Derivation for the Evolved Fuzzy Model
4 Evaluation
4.1 Wienner-Hammerstein Dynamic Process
4.2 Evolving Fuzzy Model Identification
4.3 Reference Tracking Using PFC
4.4 Disturbance Rejection Performance
5 Conclusions
References
A Method to Optimize and Automate the Distribution of Radiology Studies
1 Introduction
2 Technologies
3 Prototype System
4 Data
5 Simulation
6 Conclusions
References
Fuzzy Baselines to Stabilize Policy Gradient Reinforcement Learning
1 Introduction
2 Learning and Reinforcement Frameworks
2.1 Reinforcement Learning
2.2 Imitation Learning
2.3 Fuzzy Reinforcement Learning
3 Fuzzy Baseline Method for Policy Gradient
4 Computational Experiments
5 Conclusion
References
Fuzzy Tunes
1 Introduction
2 Review
3 Methodology
3.1 Spectral Bandwidth
3.2 Spectral Centroid
3.3 Chroma Shift
3.4 System Overview
4 Results
5 Conclusion
References
Mexican Folk Arithmetic Algorithm Makes Perfect Sense
1 Formulation of the Problem
2 Let Us Look at How Computers Add Numbers
3 Let Us Look at Practical Situations Where People Use Addition and Subtraction
4 In Many Practical Situations, We Operate Under Uncertainty
5 Conclusion
References
Fuzzy-Based, Noise-Resilient, Explainable Algorithm for Regression
1 Introduction
2 Research Objectives
3 Methodology
3.1 The Algorithm
3.2 Comparison to Other Methods
4 Results
4.1 Benchmark with Neural Networks
4.2 Training Stability
4.3 Discussion
4.4 Scalability
5 Conclusion
References
Evaluation Criteria for Noise Resilience in Regression Algorithms
1 Introduction
2 Research Objectives
3 Methodology
3.1 Framework
3.2 Conditions of Noise Resilience
3.3 NR, Global Noise Resilience Score
4 Conclusion
References
Numerical Solution for Fuzzy Partial Differential Equations with Interactive Fuzzy Boundary Conditions
1 Introduction
2 Mathematical Background
2.1 Finite Difference Method
2.2 Fuzzy Set Theory
3 Numerical Method to Solve Fuzzy Partial Differential Equation
4 Final Remarks
References
Fuzzy Logic Leads to a More Adequate Way of Processing Likert-Scale Values: Case Study of Burnout
1 Formulation of the Problem
2 Analysis of the Problem
3 Resulting Recommendation
References
Author Index

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