[内容简介]

Machine learning and data mining are inseparably connected withuncertainty. The observable data for learning is usuallyimprecise,incomplete or noisy. Uncertainty Modeling for Data MiningA Label Semantics Approach introduces 'label semantics', afuzzy-logic-based theory for modeling uncertainty. Several new datamining algorithms based on label semantics are proposed and testedon real-world datasets. A prototype interpretation of labelsemantics and new prototype-based data mining algorithms are alsodiscussed. This book offers a valuable resource for postgraduates,researchers and other professionals in the fields of data mining,fuzzy computing anduncertainty reasoning.
《基于不确定性建模的数据挖掘（英文版）》由秦曾昌、汤永川编著。

[目录]

1 Introduction
1.1 Types of Uncertainty
1.2 Uncertainty Modeling and Data Mining
1.3 Related Works
References
2 Induction and Learning
2.1 Introduction
2.2 Machine Learning
2.2.1 Searching in Hypothesis Space
2.2.2 Supervised Learning
2.2.3 Unsupervised Leaming
2.2.4 Instance-Based Learning
2.3 Data Mining and Algorithms
2.3.1 Why Do We Need Data Mining?
2.3.2 How Do We do Data Mining?
2.3.3 Artificial Neural Networks
2.3.4 Support Vector Machines
2.4 Measurement of Classifiers
2.4.1 ROC Analysis for Classification
2.4.2 Area Under the ROC Curve
2.5 Summary
References
3 Label Semantics Theory
3.1 Uncertainty Modeling with Labels
3.1.1 Fuzzy Logic
3.1.2 Computing with Words
3.1.3 Mass Assignment Theory
3.2 Label Semantics
3.2.1 Epistemic View of Label Semantics
3.2.2 Random Set Framework
3.2.3 Appropriateness Degrees
3.2.4 Assumptions for Data Analysis
3.2.5 Linguistic Translation
3.3 Fuzzy Discretization
3.3.1 Percentile-Based Discretization
3.3.2 Entropy-Based Discretization
3.4 Reasoning with Fuzzy Labels
3.4.1 Conditional Distribution Given Mass Assignments
3.4.2 Logical Expressions of Fuzzy Labels
3.4.3 Linguistic Interpretation of Appropriate Labels
3.4.4 Evidence Theory and Mass Assignment
3.5 Label Relations
3.6 Summary
References
4 Linguistic Decision Trees for Classification
4.1 Introduction
4.2 Tree Induction
4.2.1 Entropy
4.2.2 Soft Decision Trees
4.3 Linguistic Decision for Classification
4.3.1 Branch Probability
4.3.2 Classification by LDT
4.3.3 Linguistic ID3 Algorithm
4.4 Experimental Studies
4.4.1 Influence of the Threshold
4.4.2 Overlapping Between Fuzzy Labels
4.5 Comparison Studies
4.6 Merging of Branches
4.6.1 Forward Merging Algorithm
4.6.2 Dual-Branch LDTs
4.6.3 Experimental Studies for Forward Merging
4.6.4 ROC Analysis for Forward Merging
4.7 Linguistic Reasoning
4.7.1 Linguistic Interpretation of an LDT
4.7.2 Linguistic Constraints
4.7.3 Classification of Fuzzy Data
4.8 Summary
References
5 Linguistic Decision Trees for Prediction
5.1 Prediction Trees
5.2 Linguistic Prediction Trees
5.2.1 Branch Evaluation
5.2.2 Defuzzification
5.2.3 Linguistic ID3 Algorithm for Prediction
5.2.4 Forward Branch Merging for Prediction
5.3 Experimental Studies
5.3.1 3D Surface Regression
5.3.2 Abalone and Boston Housing Problem
5.3.3 Prediction of Sunspots
5.3.4 Flood Forecasting
5.4 Query Evaluation
5.4.1 Single Queries
5.4.2 Compound Queries
5.5 ROC Analysis for Prediction
5.5.1 Predictors and Probabilistic Classifiers
5.5.2 AUC Value for Prediction
5.6 Summary
References
6 Bayesian Methods Based on Label Semantics
6.1 Introduction
6.2 Naive Bayes
6.2.1 Bayes Theorem
6.2.2 Fuzzy Naive Bayes
6.3 Fuzzy Semi-Naive Bayes
6.4 Online Fuzzy Bayesian Prediction
6.4.1 Bayesian Methods
6.4.2 Online Learning
6.5 Bayesian Estimation Trees
6.5.1 Bayesian Estimation Given an LDT
6.5.2 Bayesian Estimation from a Set of Trees
6.6 Experimental Studies
6.7 Summary
References
7 Unsupervised Learning with Label Semantics
7.1 Introduction
7.2 Non-Parametric Density Estimation
7.3 Clustering
7.3.1 Logical Distance
7.3.2 Clustering of Mixed Objects
7.4 Experimental Studies
7.4.1 Logical Distance Example
7.4.2 Images and Labels Clustering
7.5 Summary
References
8 Linguistic FOIL and Multiple Attribute Hierarchy for DecisionMaking
8.1 Introduction
8.2 Rule Induction
8.3 Multi-Dimensional Label Semantics
8.4 Linguistic FOIL
8.4.1 Information Heuristics for LFOIL
8.4.2 Linguistic Rule Generation
8.4.3 Class Probabilities Given a Rule Base
8.5 Experimental Studies
8.6 Multiple Attribute Decision Making
8.6.1 Linguistic Attribute Hierarchies
8.6.2 Information Propagation Using LDT
8.7 Summary
References
9 A Prototype Theory Interpretation of Label Semantics
9.1 Introduction
9.2 Prototype Semantics for Vague Concepts
9.2.1 Uncertainty Measures about the Similarity NeighborhoodsDetermined by Vague Concepts
9.2.2 Relating Prototype Theory and Label Semantics
9.2.3 Gaussian-Type Density Function
9.3 Vague Information Coarsening in Theory of Prototypes
9.4 Linguistic Inference Systems
9.5 Summary
References
10 Prototype Theory for Learning
10.1 Introduction
10.1.1 General Rule Induction Process
10.1.2 A Clustering Based Rule Coarsening
10.2 Linguistic Modeling of Time Series Predictions
10.2.1 Mackey-Glass Time Series Prediction
10.2.2 Prediction of Sunspots
10.3 Summary
References
11 Prototype-Based Rule Systems
11.1 Introduction
11.2 Prototype-Based IF-THEN Rules
11.3 Rule Induction Based on Data Clustering and Least-SquareRegression
11.4 Rule Learning Using a Conjugate Gradient Algorithm
11.5 Applications in Prediction Problems
11.5.1 Surface Predication
11.5.2 Mackey-Glass Time Series Prediction
11.5.3 Prediction of Sunspots
11.6 Summary
References
12 Information Cells and Information Cell Mixture Models
12.1 Introduction
12.2 Information Cell for Cognitive Representation of Vague ConceptSemantics
12.3 Information Cell Mixture Model (ICMM) for SemanticRepresentation of Complex Concept
12.4 Learning Information Cell Mixture Model from Data Set
12.4.1 Objective Function Based on Positive DensityFunction..
12.4.2 Updating Probability Distribution of InformationCells...
12.4.3 Updating Density Functions of Information Cells
12.4.4 Information Cell Updating Algorithm
12.4.5 Learning Component Number of ICMM
12.5 Experimental Study
12.6 Summary
References