- 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
Editor Biography
Charu C. Aggarwal is a Research Scientist at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. His research interest during his Ph.D. years was in combinatorial optimization (network flow algorithms), and his thesis advisor was Professor James B. Orlin. He has since worked in the field of performance analysis, databases, and data mining. He has published over 200 papers in refereed conferences and journals, and has applied for or been granted over 80 patents. He is author or editor of ten books. Because of the commercial value of the aforementioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, a recipient of the IBM Outstanding Technical Achievement Award (2009) for his work on data streams, and a recipient of an IBM Research Division Award (2008) for his contributions to System S. He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining.
He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Knowledge Discovery and Data Mining, an action editor of the Data Mining and Knowledge Discovery Journal, editor-inchief of the ACM SIGKDD Explorations, and an associate editor of the Knowledge and Information Systems Journal. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He serves as the vice-president of the SIAM Activity Group on Data Mining, which is responsible for all data mining activities organized by SIAM, including their main data mining conference. He is a fellow of the IEEE and the ACM, for “contributions to knowledge discovery and data mining algorithms.”
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