基于不确定性建模的数据挖掘（英文版）
作者：秦曾昌，汤永川 著
出版时间：2013年版
《基于不确定性建模的数据挖掘（英文版）》的英文简介如下： Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise,incomplete or noisy. Uncertainty Modeling for Data Mining A Label Semantics Approach introduces label semantics, a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. 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 Learning
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
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5 Linguistic Decision Trees for Prediction
6 Bayesian Methods Based on Label Semantics
7 Unsupervised Learning with Label Semantics
8 Linguistic FOIL and Multiple Attribute Hierarchy for Decision Making
9 A prototype Theory Interpretation of Label Semantics
10 Prototype Theory for Learning
11 Prototype-Based Rule Systems
12 Information Cells and Information Cell Mixture Models
