Modeling Decision for Artificial Intelligence
8th International Conference, MDAI 2011, Changsha, Hunan, China, July 28-30, 2011, Proceedings
Published on 21. July 2011
XIII, 259 pages
978-3-642-22589-5 (ISBN)
Description
This book constitutes the proceedings of the 8th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2011, held in Changsha, China, in July 2011.
The 25 papers presented were carefully reviewed and selected from 43 submissions. The volume also contains extended abstracts of the three invited papers. The topics covered are aggregation operators and decision making; clustering and similarity; computational intelligence; and data privacy.
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Vicenc Torra | Yasuo Narukawa | Jianping Yin
Modeling Decision for Artificial Intelligence
8th International Conference, MDAI 2011, Changsha, Hunan, China, July 28-30, 2011, Proceedings
Book
07/2011
1st Edition
Springer
€53.49
Shipment within 7-9 days
Persons
Content
- Title
- Preface
- Organization
- Table of Contents
- Invited Papers
- Online Social Honeynets: Trapping Web Crawlers in OSN
- Introduction
- State of the Art
- Web Crawling Architecture
- Scheduler Algorithms
- Privacy Threats Related to Crawling Activity
- Scheduler Implications on Privacy
- Online Social Honeynets
- An Online Social Honeynet to Protect OSN from Greedy Schedulers
- Experimental Results
- Conclusions
- References
- Cost-Sensitive Learning
- References
- Evolving Graph Structures for Drug Discovery
- Fuzzy Measures and Comonotonicity on Multisets
- Introduction
- Preliminaries
- Choquet Integral and Sugeno Integral
- Generalized Fuzzy Integral
- Multisets
- Representation of Fuzzy Measures for Finite Multisets
- Comonotonicity of Multisets
- Extension of Fuzzy Measure to Multisets
- Conclusion
- References
- Regular Papers
- Aggregation Operators and Decision Making
- A Parallel Fusion Method for Heterogeneous Multi-sensor Transportation Data
- Introduction
- Related Work
- Hierarchical Evidential Fusion Model
- A Brief Introduction of D-S Evidence Theory
- Architecture of HEFM
- Algorithm of HEFM
- Parallelized Implementation
- Quadtree
- Region-Based Decomposition of Traffic Feature Data
- Fusion Task Scheduling
- Experimental Results and Analysis
- Experiments Setup
- Results and Analysis
- Conclusions and Future Work
- References
- A Dynamic Value-at-Risk Portfolio Model
- Introduction
- A Dynamic Portfolio Model
- Value-at-Risks in a Dynamic Stochastic Environment
- A Portfolio Optimization for Value-at-Risks
- A Numerical Example
- References
- Modelling Heterogeneity among Experts in Multi-criteria Group Decision Making Problems
- Introduction
- Preliminaries
- MCGDM Problems
- Heterogeneity in Group Decision Making
- Fuzzy Linguistic Approach
- A New Fuzzy Linguistic MCGDM Model Based on Heterogeneous Experts' Opinions
- Obtaining the Appropriate Set of Heterogeneously Specialized Experts and Their Preferences
- Fuzzy Linguistic MCGDM Selection Process with Heterogeneously Specialized Experts
- Conclusions
- References
- Fast Mining of Non-derivable Episode Rules in Complex Sequences
- Introduction
- Preliminaries and Problem Definition
- Preliminaries
- Problem Definition
- Frequency Measurement
- The Mining Algorithm
- Multiple Layered Maximal Frequent Episodes
- Algorithm Description
- A Running Example
- Experimental Results
- Related Work
- Conclusion
- References
- Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems
- Introduction
- Proposed New Trading Strategy Framework
- The Technical Trading Rule (or Filtering) Component
- The Abstaining-Classifier Component
- Strategy Execution
- Methodology
- The Four Experimental Conditions
- Evaluation Measures
- Evaluation
- Datasets
- Results
- Conclusion
- References
- Semi-supervised Dimensionality Reduction via Harmonic Functions
- Introduction
- Notations and Harmonic Function
- The Algorithm
- Computing the State by Harmonic Function
- Constructing a Weighted Complete Graph
- Deriving Projection Matrix
- Analysis and Extensions
- Experiments and Discussions
- Conclusions and Future Works
- References
- Clustering
- Semi-supervised Agglomerative Hierarchical Clustering with Ward Method Using Clusterwise Tolerance
- Introduction
- Preparation
- Agglomerative Hierarchical Clustering
- Centroid Method
- Ward Method
- Pairwise Constraints
- Clusterwise Tolerance Based Pairwise Constraints
- Clusterwise Tolerance
- Clusterwise Tolerance Based Pairwise Constraints
- Semi-supervised Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints
- Centroid Method
- Ward Method
- Algorithm
- Numerical Examples
- Conclusions
- References
- Agglomerative Clustering Using Asymmetric Similarities
- Introduction
- Agglomerative Hierarchical Clustering
- Preliminaries
- Asymmetric Similarity Measures
- Asymmetric Average Link
- A Probabilistic Model
- Dendrogram without Reversals
- Asymmetric Dendrogram
- Application to Real Data
- Conclusion
- References
- On Hard c-Means Using Quadratic Penalty-Vector Regularization for Uncertain Data
- Introduction
- Preliminaries
- Tolerance and Penalty Vectors
- Hard c-Means
- Hard c-Means Using Quadratic Penalty-Vector Regularization
- Optimal Solutions of HCMP
- Algorithm
- Sequential Extraction Hard c-Means Using Quadratic Penalty-Vector Regularization
- Sequential Extraction Hard c-Means
- Sequential Extraction Hard c-Means Using Quadratic Penalty-Vector Regularization
- Numerical Examples
- Results
- Consideration
- Conclusion
- References
- Grey Synthetic Clustering Method for DoS Attack Effectiveness Evaluation
- Introduction
- Index System of Effectiveness Evaluation of DoS Attack
- Grey Synthetic Clustering Evaluation Model
- Example and Verification
- Conclusion
- References
- Fuzzy-Possibilistic Product Partition: A Novel Robust Approach to c-Means Clustering
- Introduction
- Preliminaries
- Fuzzy c-Means Clustering
- Possibilistic c-Means Clustering
- Existing Fuzzy-Possibilistic Mixture Partitions
- Methods
- Intuition
- The Proposed Clustering Model
- The Alternative Optimization Algorithm of FP3CM
- Results and Discussion
- Two Clusters and One Outlier Input Vector
- Accuracy Test with Nine Regular Clusters and an Outlier
- Numerical Tests Using IRIS Data
- Conclusions
- References
- Computational Intelligence and Data Mining
- A Novel and Effective Approach to Shape Analysis: Nonparametric Representation, De-noising and Change- Point Detection, Based on Singular-Spectrum Analysis
- The Classical Computational Geometry Approach to Sampling Time Series from Planar Closed Contours
- A Novel and Effective Approach to Shape Analysis: Nonparametric Representation, De-noising and Change-Point Detection, Based on Singular-Spectrum Analysis
- An overview of Singular-Spectrum Analysis
- The First Setting: Applying SSA to a Shape Signature Encoded by Sampling a Real-Valued Time Series from a Radius-Vector Contour Function
- The Second Setting: Applying SSA to a Shape Signature Encoded by Sampling a Complex-Valued Time Series from the Contour Itself, Represented in the Complex Plane
- The Third Setting: Applying SSA to a Shape Signature Encoded by Sampling Two Real-Valued Time Series from the x and y Coordinates
- Conclusion
- References
- A SSA-Based New Framework Allowing for Smoothing and Automatic Change-Points Detection in the Fuzzy Closed Contours of 2D Fuzzy Objects
- Representations of Fuzzy Objects: Fuzzy Regions vs. Fuzzy Closed Curves
- Geodesic vs. Euclidian Fuzzy Paths and Distances
- A Novel Approach to Decomposing and Reconstructing a Fuzzy Shape and to Automatic Change Point-Detection, Based on SSA
- Conclusion
- References
- Possibilistic Linear Programming Using General Necessity Measures Preserves the Linearity
- Introduction
- Necessity Measures and Logical Connectives
- Possibilistic Linear Programming Problems
- Reduction to a Linear Programming Problem
- Examples of Implication Functions
- Concluding Remarks
- References
- An Efficient Hybrid Approach to Correcting Errors in Short Reads
- Introduction
- Methods
- Problem
- Solution
- Algorithm
- Searching the Correct Overlapping Regions
- Forming Multiple Alignments and Correcting Errors
- Choosing Parameters
- The Full Algorithm and Complexity
- Evaluation
- Conclusion
- References
- Data Privacy
- Rule Protection for Indirect Discrimination Prevention in Data Mining
- Introduction
- Discrimination-Aware Data Mining
- Contribution and Paper Organization
- Discovering Discrimination
- Background
- Indirect Discrimination Formalization
- A Proposal for Indirect Discrimination Prevention
- Data Transformation Method
- Experimental Evaluation
- Utility Measures
- Results
- Conclusions
- References
- A Comparison of Two Different Types of Online Social Network from a Data Privacy Perspective
- Introduction
- State of the Art and Related Work
- Definition of Basic Data and Derived Factors
- Descriptive Statistics for Derived Factors
- Data Privacy: Information Loss and Risk of Disclosure - Enron vs. Facebook
- Information Loss
- Risk of Disclosure
- Conclusions
- References
- On the Declassification of Confidential Documents
- Introduction
- Motivations and Preliminary Notions
- Overview of Popular Protection Methods
- Named Entity Recognition
- Anonymization of Unstructured Documents
- Private Entity Recognition
- Some Consideration on Entity Recognition
- Entity Protection
- Some Desired Properties of Anonymized Documents
- Experiments
- Conclusions
- References
- Uncovering Community Structure in Social Networks by Clique Correlation
- Introduction
- Uncovering Community Structures Based on Modularity
- Measuring the Correlation
- Building the Vertex-Clique Bipartite Graph
- Weighting the Edges
- Covariance and Correlation Coefficients
- Special Case: Modularity
- Uncovering Communities Structure by Eigenvectors
- Experiment
- Covariance vs. Correlation Coefficients
- Experiment on Computer Generate Networks
- Real Social Networks
- Conclusion
- References
- Author Index
