
Integrated Uncertainty in Knowledge Modelling and Decision Making
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The 37 revised full papers presented were carefully reviewed and selected from 93 submissions. The papers deal with all aspects of uncertainty modelling and management and are organized in topical sections on uncertainty management and decision support; econometrics; machine learning; machine learning applications; and statistical methods.
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Content
- Intro
- Preface
- Organization
- Invited Speakers
- Choquet Integral in Decision Making and Metric Learning
- Logic for Thinking - From Mathematical Logic to Grammatical Logic
- Robust Decisions Under Uncertainty with a Rule Preference Model of Multiple Decision Makers
- Variations and Generalizations of Fuzzy c-Means Clustering
- Contents
- Uncertainty Management and Decision Support
- Scientometric Indices Based on Integrals and Their Adaptation in Different Domains
- 1 Introduction
- 2 Universal Integrals, Scientometric Records and h-index
- 3 Some Other Scientometric Indices
- 4 Modification of Scientometric Indices for Different Domains
- 5 Concluding Remarks
- References
- Normalization of Multiple Efficiency Intervals by Interval Data Envelopment Analysis from Different Frameworks
- 1 Introduction
- 2 Interval Data Envelopment Analysis
- 3 Normalization of Efficiency Intervals
- 4 Numerical Example
- 5 Conclusion
- References
- Overtime Assignment and Job Satisfaction in Noise-Safe Job Rotation Scheduling
- Abstract
- 1 Introduction
- 2 Literature Review
- 3 Mathematical Model Formulation
- 3.1 Scenario 1: Minimizing Cost
- 3.2 Scenario 2: Maximizing the Minimum Satisfaction Level
- 3.3 Scenario 3: Multi-objective Optimization
- 4 Numerical Example
- 5 Result and Discussion
- 6 Conclusion
- Acknowledgement
- References
- Ambiguity Measures for Preference-Based Decision Viewpoints
- 1 Introduction
- 2 Decision Modeling by Fuzzy Preferences
- 2.1 The Standard Fuzzy Model
- 2.2 Preference-Aversion Fuzzy Model
- 2.3 Modeling Decisions by Opposite Concepts
- 3 Decision Viewpoints
- 4 Ambiguity Measures for Decision Modeling
- 4.1 Fuzziness and Ambiguity Measures
- 4.2 Fuzzy-Ambiguity Measures
- 4.3 Ambiguity Measures for Decision Viewpoints
- 5 Final Comments
- References
- Logics of Dominance for Reasoning About Multi-criteria Decisions
- 1 Introduction
- 2 Preliminaries
- 2.1 Modal Logic
- 2.2 Rough Set Theory
- 2.3 Dominance-Based Rough Set Approach
- 3 A Logic of Multi-criteria Preference
- 3.1 A Logic of Multi-criteria Total Preorder Preference
- 4 A Logic of Modal Dominance
- 5 Extensions and Variants
- 5.1 Intersection of Arbitrary Modalities
- 5.2 Boolean Combination of Modalities
- 5.3 Hybrid Logic
- 6 Conclusion
- References
- GY MEDIC: Analysis and Rehabilitation System for Patients with Facial Paralysis
- Abstract
- 1 Introduction
- 2 System Structure
- 3 Facial Features Detection
- 4 Virtual Environment
- 5 Experimental Results
- 5.1 Module I: Analysis
- 5.2 Module II: Rehabilitation
- 6 Conclusion
- Acknowledgements
- References
- Revealed Preference for Network Design in Bilevel Linear Programming
- 1 Introduction
- 2 Bilevel Linear Program for Network Design
- 2.1 Problem Formulation
- 2.2 The Decision Norms in the Network Flows
- 3 Revealed Preference
- 3.1 Mathematical Setting
- 3.2 Data Aggregation Model
- 3.3 BLP Problem Reformulation
- 4 Conclusion
- References
- Rule Induction Based on Rough Sets from Possibilistic Data Tables
- 1 Introduction
- 2 Rough Sets in Complete Information Tables
- 3 Rough Sets in Possibilistic Information Tables
- 4 Rule Induction in Possibilistic Information Tables
- 5 Conclusions
- References
- Probability-Based Approach Explains (and Even Improves) Heuristic Formulas of Defuzzification
- 1 Formulation of the Problem
- 2 Probability-Based Approach Explains Heuristic Formulas of Defuzzification
- 3 Let Us Use the Probability-Based Justification to Improve the Heuristic Formulas for Defuzzification
- 4 Probability-Based Approach Explains Heuristic Formulas of Optimization Under Fuzzy Constraints
- References
- Three Valued Representation of Opinions in Affective Design
- 1 Introduction
- 2 Vagueness
- 3 Kleene's Three Valued Logic and Belief Pairs
- 3.1 Conditional Kleene Measures
- 4 Representing Opinions
- 5 Kansei Case Study
- 6 Ranking Designs
- 7 Conclusions
- References
- Sampling Strategies for Fuzzy RANSAC Algorithm Based on Reinforcement Learning
- Abstract
- 1 Introduction
- 2 Computational Robust Estimation Techniques
- 2.1 RANSAC Algorithm
- 2.2 LMedS Algorithm
- 3 Fuzzy RANSAC Algorithm Based on Reinforcement Learning
- 4 Sampling Strategies for Fuzzy RANSAC Algorithm
- 4.1 Roulette Strategy and Soft-Max Strategy
- 4.2 e-Greedy Strategy
- 4.3 e-Roulette Strategy
- 4.4 Modeling Procedures
- 5 Experimental Results
- 5.1 Nonlinear Modeling Results Using Synthetic Data
- 5.2 Results of Camera Modeling
- 6 Conclusions
- References
- Interpretation of Variable Consistency Dominance-Based Rough Set Approach by Minimization of Asymmetric Loss Function
- 1 Introduction
- 2 Variable-Consistency Dominance-Based Rough Set Approach
- 2.1 Decision Table and DRSA
- 2.2 VC-DRSA
- 3 Empirical Risk Minimization for VC-DRSA
- 4 Concluding Remarks
- References
- Econometrics
- An Econometric Study of Inbound Tourism Demand in Hong Kong, Macao and Taiwan: A Case Study of Mainland China
- 1 Introduction
- 2 Methodology
- 2.1 Data and Variable Selection
- 2.2 Model Specification
- 3 Empirical Results
- 4 Conclusion
- References
- The Dependence Structure and Portfolio Optimization in Economic Cycles: An Application in ASEAN Stock Market
- 1 Introduction
- 2 Methodology
- 2.1 The Markovian Switching Model
- 2.2 AR-GARCH Model
- 2.3 The Vine Copulas Construction
- 2.4 The Maximum Entropy Bootstrapping
- 2.5 The Markowitz Portfolio Selection Model
- 3 Empirical Results of Research
- 3.1 The Data Description
- 3.2 The Estimation of AR-GARCH in Bull and Bear Markets
- 3.3 The Results of Co-movement Structure of D-Vine Trees in Bull and Bear Markets
- 3.4 The Markovian Optimization for Portfolio Selection
- 4 Conclusion
- References
- Hedging Benefit of Safe-Haven Gold in Terms of Co-skewness and Covariance in Stock Market
- 1 Introduction
- 2 Methodology
- 2.1 Markov Switching Dynamic Conditional Correlation GARCH
- 2.2 Generating Co-skewness and Covariance Processes
- 3 Data Description
- 4 Empirical Summarization
- 4.1 Results on Regime-Switching Model Estimation
- 4.2 The Effects of Co-skewness and Correlation on Stock Returns
- 5 Conclusion
- References
- Markov Switching Beta-skewed-t EGARCH
- 1 Introduction
- 2 Methodology
- 2.1 Beta-skewed-t GARCH Model
- 2.2 Markov Switching Beta-skewed-t GARCH Model
- 3 Forecasting Methodology and AIC and BIC Weights
- 3.1 Forecasting Evaluation Method
- 3.2 Model Selection: Akaike Weights and Bayesian Weights
- 4 Estimate Results
- 4.1 Data Description
- 4.2 Forecast Evaluation
- 4.3 AIC and BIC Weights
- 4.4 Estimation Parameter Results
- 5 Conclusion
- References
- Building Fuzzy Levy-GJR-GARCH American Option Pricing Model
- 1 Introduction
- 2 Parabolic Fuzzy Variable
- 3 Fuzzy Levy-GJR-GARCH American Option Pricing Model
- 3.1 The Process of the Underlying Asset Price
- 3.2 The Risk-Neutral Conversion of the Underlying Asset Pricing
- 4 The Algorithm Design for Fuzzy American Option Pricing Model
- 4.1 Fuzzy Simulation Technology
- 4.2 Fuzzy Least Squares Monte Carlo Algorithm
- 5 Empirical Analysis
- 5.1 Source of Data
- 5.2 Parameter Estimation
- 5.3 Empirical Result Analysis
- 6 Conclusions
- References
- Nonlinear Dependence Structure in Emerging and Advanced Stock Markets
- Abstract
- 1 Introduction
- 2 The Model
- 2.1 Bivariate Copula
- 2.2 Smoot Transition Copula
- 2.3 Markov Switching Copula
- 3 Estimation
- 4 Marginal Distribution Modeling
- 5 Empirical Study
- 5.1 Data and Summary Statistics
- 6 Model Comparison and the Estimation Results
- 7 Conclusion
- Acknowledgement
- References
- Mean Absolute Deviation Portfolio Frontiers with Interval-Valued Returns
- 1 Interval Linear Programming
- 1.1 Notations
- 1.2 Feasibility
- 1.3 Range of Optimal Values
- 2 Mean Absolute Deviation (MAD) Portfolio Model
- 2.1 Development of Model with Real-Valued Returns
- 2.2 Model with Interval-Valued Returns
- 3 Portfolio Frontiers
- 3.1 Range of Target Portfolio Returns
- 3.2 Range of Optimal Portfolio Risks
- 4 Examples and Numerical Results
- 4.1 Rates of Return
- 4.2 Lower Bounds on Expected Returns
- 4.3 Upper Bounds on Expected Returns
- 4.4 Frontiers of 9-Asset Portfolio
- 5 Discussion and Conclusion
- References
- The Impact of Economic Growth, Energy Consumption and Trade Openness on Carbon Emissions: An Empirical Analysis in China
- 1 Introduction
- 2 Data and Model
- 3 Methodology
- 4 Empirical Results
- 5 Conclusion
- References
- Machine Learning
- NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances
- 1 Introduction
- 2 NIS-Apriori-Based Rule Generation in NISs
- 2.1 DIS-Apriori-Based Rule Generation in DISs
- 2.2 NIS-Apriori-Based Rule Generation in NISs
- 2.3 Problem on Minor Rule Generation
- 3 NIS-Apriori Algorithm with a Target Descriptor
- 4 Some Experiments
- 4.1 Discussion
- 5 Concluding Remarks
- References
- Utilization of Imprecise Rules for Privacy Protection
- 1 Introduction
- 2 Rough Set Approach and Imprecise Rule Induction
- 3 The Proposed Data Anonymization
- 4 Numerical Experiments
- 4.1 Outline
- 4.2 Privacy Preservation
- 4.3 Data Usability in Classification
- 5 Conclusion
- References
- Approximate Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering
- 1 Introduction
- 2 Multiclass Classification
- 2.1 SVM Maximizing Functional Margins
- 2.2 SVM Maximizing Geometric Margins
- 3 Approximate MMSVM
- 4 AMMSVM Based on K-Means Clustering
- 4.1 k-Means Clustering
- 4.2 Dimension Reduction Based on k-Means Clustering
- 4.3 Solving Based on Reference Point Method
- 4.4 Comparison of Computational Complexities
- 5 Numerical Experiments
- 6 Conclusion
- References
- Generative Cooperative Net for Image Generation and Data Augmentation
- 1 Introduction
- 2 Related Work
- 3 Method and Model Architecture
- 3.1 Objective Function
- 3.2 Image Augmentation
- 4 Experimental Studies
- 4.1 Dataset
- 4.2 Model Training
- 4.3 Result Analysis
- 5 Conclusions and Future Work
- References
- Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers
- 1 Introduction
- 2 Classifier Development
- 3 Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness
- 3.1 Procedure Outline
- 3.2 Step 1: Near-Boundary Sample Selection
- 3.3 Step 2: Uncertainty Measure Computation
- 4 Experimental Evaluation
- 4.1 Classifiers
- 4.2 Datasets
- 4.3 Hyperparameters
- 4.4 Results on Synthetic Datasets
- 4.5 Results on Real-Life Datasets
- 5 Conclusion
- References
- Averaged Logits: An Weakly-Supervised Approach to Use Ratings to Train Sentence-Level Sentiment Classifiers
- 1 Introduction
- 2 Related Work
- 2.1 Sentence-Level Sentiment Classification Based on Labelled Sentences
- 2.2 Exploiting Document-Level Sentiment Labels for Sentence-Level Sentiment Classification
- 3 Averaged Logit
- 3.1 RNN for Sentence-Level Sentiment Classification
- 3.2 RNN Models Based on Averaged Logits
- 4 Evaluation
- 4.1 Comparison Against Models Using Ratings as Supervision Signal
- 4.2 Comparison Against Models Supervised by Sentence-Level Sentiments
- 5 Conclusion
- References
- Convolutional Neural Networks with Interpretable Kernels
- 1 Motivation and Formulation of the Problem
- 2 The F-transform of a Higher Degree (Fm-transform)
- 2.1 Fuzzy partition
- 2.2 Space L2(Ak)
- 2.3 Fm-transform
- 2.4 F2-transform in the Convolutional Form
- 3 FT-Based LeNet-5: Architecture and Efficiency
- 3.1 Efficiency of FTNet
- 3.2 Semantic Meaning of Principal Kernels in Convolutional Layers
- 4 Conclusion and the Future Work
- References
- Machine Learning Applications
- Ties Between Mined Structural Patterns in Program and Their Identifier Names
- 1 Introduction
- 2 Background
- 3 Algorithm
- 3.1 Main Components of Our Proposal
- 3.2 Feature Representation
- 3.3 Feature Selection
- 3.4 Visualization
- 4 Experiments
- 4.1 Experimental Setup
- 4.2 Experimental Results
- 5 Conclusion
- References
- Big Data and Machine Learning for Economic Cycle Prediction: Application of Thailand's Economy
- Abstract
- 1 Introduction
- 2 The Objective and Scope of Research
- 3 Theory and Methodology
- 3.1 The Fundamental Concept of Machine Learning in Data Science
- 3.2 Algorithms for Machine Learning Analyses
- 3.3 Model Validation
- 4 Empirical Results
- 4.1 Descriptive Information
- 4.2 The Results of Machine Learning Algorithm
- 5 Conclusion
- References
- A Comparative Study on SOM-Based Visualization of Potential Technical Solutions Using Fuzzy Bag-of-Words and Co-occurrence Probability of Technical Words
- 1 Introduction
- 2 Brief Review on SOM-Based Visualization of Potential Technical Solutions and Fuzzy Bag-of-Words Model
- 2.1 Preprocessing
- 2.2 SOM-Based Visualization of Connection Among Technical Terms
- 2.3 Fuzzy Bag-of-Words
- 3 Comparative Experiments
- 3.1 Conventional SOM Visualization
- 3.2 SOM of word2vec Representation
- 3.3 FBoW-Based SOM Visualization
- 4 Conclusion
- References
- Fuzzy Co-clustering for Categorization of Subjects in Questionnaire Considering Responsibility of Each Question
- 1 Introduction
- 2 FCM-Type Fuzzy Co-clustering
- 3 Fuzzy Co-clustering for Categorization of Subjects in Questionnaire
- 3.1 Proposed Objective Function
- 3.2 Updating Rules
- 4 Experimental Results
- 4.1 Experimental Data Set
- 4.2 Conventional FCCM Partition
- 4.3 Partitions by Proposed Algorithm
- 5 Conclusion
- References
- Extracting Access Patterns with Hierarchical Latent Tree Analysis: An Empirical Study on an Undergraduate Programming Course
- 1 Introduction
- 2 Hierarchical Latent Tree Analysis
- 3 Extracting Access Patterns
- 3.1 Basic Version
- 3.2 Extended Version with Access Periods
- 4 Regression with Access Patterns
- 5 Empirical Evaluation
- 5.1 Student Access Patterns
- 5.2 Prediction of Student Scores
- 6 Related Work
- 7 Conclusion
- References
- Measuring Hotel Review Sentiment: An Aspect-Based Sentiment Analysis Approach
- 1 Introduction
- 2 Literature Reviews
- 2.1 Sentiment Analysis
- 2.2 Aspect Extraction and Grouping
- 2.3 Aspect Sentiment Classification
- 3 Methodology
- 3.1 Proposed Framework
- 3.2 BiLSTM-CRF for the Combining ATE-PC Task
- 3.3 Latent Dirichlet Allocation for Topic Modeling
- 3.4 Grouping and Refining Aspects
- 4 Empirical Results
- 4.1 Dataset
- 4.2 Annotated Hospitality Dataset
- 4.3 The Combining ATE-PC Task Results
- 4.4 Topic Modeling and Grouping with LDA
- 4.5 Inferring Refined Topics and Summarization
- 5 Conclusions
- References
- Factors Affecting Carbon Emissons in the G7 and BRICS Countries: Evidence from Quantile Regression
- 1 Introduction
- 2 Literature Review and Theoretical Background
- 2.1 Literature Review
- 2.2 Theoretical Background
- 3 Methodology
- 3.1 Methodology and Data
- 3.2 Data
- 4 Empirical Findings
- 4.1 Panel Root Test Results
- 4.2 Panel Cointegration Results
- 4.3 Quantile Regression
- 5 Conclusion
- References
- Statistical Methods
- Construction of Stable Hierarchy Organization from the Perspective of the Maximum Deng Entropy
- 1 Introduction
- 2 Preliminaries
- 2.1 Framework of Dempster-Shafer Evidence Theory
- 2.2 Deng Entropy
- 3 Construction of Classical Hierarchy Organization
- 3.1 Generate Classical Construction of Hierarchy Organization
- 3.2 Problems of Construction of Classical Hierarchy Organization
- 4 Explanation of the Maximum Deng Entropy
- 5 Proposed Construction of Hierarchy Organization from the Perspective of the Maximum Deng Entropy
- 5.1 Construction of Hierarchy Organization Using the Maximum Deng Entropy
- 5.2 Measuring the Ability of the Department of Hierarchy Organization Using the Maximum Deng Entropy
- 6 Numerical Examples
- 7 Conclusion
- References
- Restricted Similarity Functions, Distances and Entropies with Intervals Using Total Orders
- 1 Introduction
- 2 Preliminaries
- 3 Restricted Equivalence Functions in L([0, 1]) with Respect to a Total Order
- 3.1 Dissimilarity Functions in L([0, 1]) with Respect to a Total Order
- 4 Similarity Measures, Distances and Entropy Measures in L([0, 1]) with Respect to a Total Order
- 5 Conclusions
- References
- Author Index
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