- Preliminaries
- Series
- Dedication
- Editor Biography
- Contributors
- Preface
- Chapter 1 An Introduction to Data Classification
- 1.1 Introduction
- 1.2 Common Techniques in Data Classification
- 1.3 Handing Different Data Types
- 1.4 Variations on Data Classification
- 1.5 Discussion and Conclusions
- Bibliography
- Figure 1.1
- Table 1.1
- Chapter 2 Feature Selection for Classification: A Review
- Chapter 3 Probabilistic Models for Classification
- Chapter 4 Decision Trees: Theory and Algorithms
- Chapter 5 Rule-Based Classification
- 5.1 Introduction
- 5.2 Rule Induction
- 5.3 Classification Based on Association Rule Mining
- 5.4 Applications
- 5.5 Discussion and Conclusion
- Bibliography
- Table 5.1
- Chapter 6 Instance-Based Learning: A Survey
- 6.1 Introduction
- 6.2 Instance-Based Learning Framework
- 6.3 The Nearest Neighbor Classifier
- 6.4 Lazy SVM Classification
- 6.5 Locally Weighted Regression
- 6.6 Lazy Naive Bayes
- 6.7 Lazy Decision Trees
- 6.8 Rule-Based Classification
- 6.9 Radial Basis Function Networks: Leveraging Neural Networks for Instance-Based Learning
- 6.10 Lazy Methods for Diagnostic and Visual Classification
- 6.11 Conclusions and Summary
- Bibliography
- Figure 6.1
- Chapter 7 Support Vector Machines
- Chapter 8 Neural Networks: A Review
- 8.1 Introduction
- 8.2 Fundamental Concepts
- 8.3 Single-Layer Neural Network
- 8.4 Kernel Neural Network
- 8.5 Multi-Layer Feedforward Network
- 8.6 Deep Neural Networks
- 8.7 Summary
- Acknowledgements
- Bibliography
- Figure 8.1
- Table 8.1
- Chapter 9 A Survey of Stream Classification Algorithms
- Chapter 10 Big Data Classification
- Chapter 11 Text Classification
- 11.1 Introduction
- 11.2 Feature Selection for Text Classification
- 11.2.1 Gini Index
- 11.2.2 Information Gain
- 11.2.3 Mutual Information
- 11.2.4 χ2-Statistic
- 11.2.5 Feature Transformation Methods: Unsupervised and Supervised LSI
- 11.2.6 Supervised Clustering for Dimensionality Reduction
- 11.2.7 Linear Discriminant Analysis
- 11.2.8 Generalized Singular Value Decomposition
- 11.2.9 Interaction of Feature Selection with Classification
- 11.3 Decision Tree Classifiers
- 11.4 Rule-Based Classifiers
- 11.5 Probabilistic and Naive Bayes Classifiers
- 11.6 Linear Classifiers
- 11.7 Proximity-Based Classifiers
- 11.8 Classification of Linked and Web Data
- 11.9 Meta-Algorithms for Text Classification
- 11.10 Leveraging Additional Training Data
- 11.11 Conclusions and Summary
- Bibliography
- Figure 11.1
- Chapter 12 Multimedia Classification
- Chapter 13 Time Series Data Classification
- Chapter 14 Discrete Sequence Classification
- Chapter 15 Collective Classification of Network Data
- Chapter 16 Uncertain Data Classification
- Chapter 17 Rare Class Learning
- 17.1 Introduction
- 17.2 Rare Class Detection
- 17.3 The Semi-Supervised Scenario: Positive and Unlabeled Data
- 17.4 The Semi-Supervised Scenario: Novel Class Detection
- 17.5 Human Supervision
- 17.6 Other Work
- 17.7 Conclusions and Summary
- Bibliography
- Figure 17.1
- Chapter 18 Distance Metric Learning for Data Classification
- 18.1 Introduction
- 18.2 The Definition of Distance Metric Learning
- 18.3 Supervised Distance Metric Learning Algorithms
- 18.3.1 Linear Discriminant Analysis (LDA)
- 18.3.2 Margin Maximizing Discriminant Analysis (MMDA)
- 18.3.3 Learning with Side Information (LSI)
- 18.3.4 Relevant Component Analysis (RCA)
- 18.3.5 Information Theoretic Metric Learning (ITML)
- 18.3.6 Neighborhood Component Analysis (NCA)
- 18.3.7 Average Neighborhood Margin Maximization (ANMM)
- 18.3.8 Large Margin Nearest Neighbor Classifier (LMNN)
- 18.4 Advanced Topics
- 18.5 Conclusions and Discussions
- Bibliography
- Table 18.1
- Chapter 19 Ensemble Learning
- Chapter 20 Semi-Supervised Learning
- Chapter 21 Transfer Learning
- 21.1 Introduction
- 21.2 Transfer Learning Overview
- 21.3 Homogenous Transfer Learning
- 21.4 Heterogeneous Transfer Learning
- 21.5 Transfer Bounds and Negative Transfer
- 21.6 Other Research Issues
- 21.7 Applications of Transfer Learning
- 21.8 Concluding Remarks
- Bibliography
- Figure 21.1
- Table 21.1
- Chapter 22 Active Learning: A Survey
- 22.1 Introduction
- 22.2 Motivation and Comparisons to Other Strategies
- 22.3 Querying Strategies
- 22.4 Active Learning with Theoretical Guarantees
- 22.5 Dependency-Oriented Data Types for Active Learning
- 22.6 Advanced Methods
- 22.6.1 Active Learning of Features
- 22.6.2 Active Learning of Kernels
- 22.6.3 Active Learning of Classes
- 22.6.4 Streaming Active Learning
- 22.6.5 Multi-Instance Active Learning
- 22.6.6 Multi-Label Active Learning
- 22.6.7 Multi-Task Active Learning
- 22.6.8 Multi-View Active Learning
- 22.6.9 Multi-Oracle Active Learning
- 22.6.10 Multi-Objective Active Learning
- 22.6.11 Variable Labeling Costs
- 22.6.12 Active Transfer Learning
- 22.6.13 Active Reinforcement Learning
- 22.7 Conclusions
- Bibliography
- Figure 22.1
- Chapter 23 Visual Classification
- Chapter 24 Evaluation of Classification Methods
- Chapter 25 Educational and Software Resources for Data Classification
Contributors
Charu C. Aggarwal
IBM T. J. Watson Research Center
Yorktown Heights, New York
Salem Alelyani
Arizona State University
Tempe, Arizona
Mohammad Al Hasan
Indiana University - Purdue University
Indianapolis, Indiana
Alain Biem
IBM T. J. Watson Research Center
Yorktown Heights, New York
Shiyu Chang
University of Illinois at Urbana-Champaign
Urbana, Illinois
Yi Chang
Yahoo! Labs
Sunnyvale, California
Reynold Cheng
The University of Hong Kong
Hong Kong
Hongbo Deng
Yahoo! Research
Sunnyvale, California
Giorgio Maria Di Nunzio
University of Padua
Padova, Italy
Wei Fan
Huawei Noah’s Ark Lab
Hong Kong
Yixiang Fang
The University of Hong Kong
Hong Kong
Jing Gao
State University of New York at Buffalo
Buffalo, New York
Quanquan Gu
University of Illinois at Urbana-Champaign
Urbana, Illinois
Dimitrios Gunopulos
University of Athens
Athens, Greece
Jiawei Han
University of Illinois at Urbana-Champaign
Urbana, Illinois
Wei Han
University of Illinois at Urbana-Champaign
Urbana, Illinois
Thomas S. Huang
University of Illinois at Urbana-Champaign
Urbana, Illinois
Ruoming Jin
Kent State University
Kent, Ohio
Xiangnan Kong
University of Illinois at Chicago
Chicago, Illinois
Dimitrios Kotsakos
University of Athens
Athens, Greece
Victor E. Lee
John Carroll University
University Heights, Ohio
Qi Li
State University of New York at Buffalo
Buffalo, New York
Xiao-Li Li
Institute for Infocomm Research
Singapore
Yaliang Li
State University of New York at Buffalo
Buffalo, New York
Bing Liu
University of Illinois at Chicago
Chicago, Illinois
Huan Liu
Arizona State University
Tempe, Arizona
Lin Liu
Kent State University
Kent, Ohio
Xianming Liu
University of Illinois at Urbana-Champaign
Urbana, Illinois
Ben London
University of Maryland
College Park, Maryland
Sinno Jialin Pan
Institute for Infocomm Research
Sinpapore
Pooya Khorrami
University of Illinois at Urbana-Champaign
Urbana, Illinois
Chih-Jen Lin
National Taiwan University
Taipei, Taiwan
Matthias Renz
University of Munich
Munich, Germany
Kaushik Sinha
Wichita State University
Wichita, Kansas
Yizhou Sun
Northeastern University
Boston, Massachusetts
Jiliang Tang
Arizona State University
Tempe, Arizona
Ankur Teredesai
University of Washington
Tacoma, Washington
Hanghang Tong
City University of New York
New York, New York
Nele Verbiest
Ghent University
Belgium
Karel Vermeulen
Ghent University
Belgium
Fei Wang
IBM T. J. Watson Research Center
Yorktown Heights, New York
Po-Wei Wang
National Taiwan University
Taipei, Taiwan
Ning Xu
University of Illinois at Urbana-Champaign
Urbana, Illinois
Philip S. Yu
University of Illinois at Chicago
Chicago, Illinois
ChengXiang Zhai
University of Illinois at Urbana-Champaign
Urbana, Illinois
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