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Data Classification: Algorithms and Applications
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Data Classification: Algorithms and Applications
by Charu C. Aggarwal
Data Classification
Preliminaries
Series
Dedication
Editor Biography
Contributors
Preface
Chapter 1 An Introduction to Data Classification
1.1 Introduction
1.2 Common Techniques in Data Classification
1.2.1 Feature Selection Methods
1.2.2 Probabilistic Methods
1.2.3 Decision Trees
1.2.4 Rule-Based Methods
1.2.5 Instance-Based Learning
1.2.6 SVM Classifiers
1.2.7 Neural Networks
1.3 Handing Different Data Types
1.3.1 Large Scale Data: Big Data and Data Streams
1.3.1.1 Data Streams
1.3.1.2 The Big Data Framework
1.3.2 Text Classification
1.3.3 Multimedia Classification
1.3.4 Time Series and Sequence Data Classification
1.3.5 Network Data Classification
1.3.6 Uncertain Data Classification
1.4 Variations on Data Classification
1.4.1 Rare Class Learning
1.4.2 Distance Function Learning
1.4.3 Ensemble Learning for Data Classification
1.4.4 Enhancing Classification Methods with Additional Data
1.4.4.1 Semi-Supervised Learning
1.4.4.2 Transfer Learning
1.4.5 Incorporating Human Feedback
1.4.5.1 Active Learning
1.4.5.2 Visual Learning
1.4.6 Evaluating Classification Algorithms
1.5 Discussion and Conclusions
Bibliography
Figure 1.1
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Figure 1.5
Table 1.1
Table 1.1
Chapter 2 Feature Selection for Classification: A Review
2.1 Introduction
2.1.1 Data Classification
2.1.2 Feature Selection
2.1.3 Feature Selection for Classification
2.2 Algorithms for Flat Features
2.2.1 Filter Models
2.2.2 Wrapper Models
2.2.3 Embedded Models
2.3 Algorithms for Structured Features
2.3.1 Features with Group Structure
2.3.2 Features with Tree Structure
2.3.3 Features with Graph Structure
2.4 Algorithms for Streaming Features
2.4.1 The Grafting Algorithm
2.4.2 The Alpha-Investing Algorithm
2.4.3 The Online Streaming Feature Selection Algorithm
2.5 Discussions and Challenges
2.5.1 Scalability
2.5.2 Stability
2.5.3 Linked Data
Acknowledgments
Bibliography
Figure 2.1
Figure 2.1
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Chapter 3 Probabilistic Models for Classification
3.1 Introduction
3.2 Naive Bayes Classification
3.2.1 Bayes’ Theorem and Preliminary
3.2.2 Naive Bayes Classifier
3.2.3 Maximum-Likelihood Estimates for Naive Bayes Models
3.2.4 Applications
3.3 Logistic Regression Classification
3.3.1 Logistic Regression
3.3.2 Parameters Estimation for Logistic Regression
3.3.3 Regularization in Logistic Regression
3.3.4 Applications
3.4 Probabilistic Graphical Models for Classification
3.4.1 Bayesian Networks
3.4.1.1 Bayesian Network Construction
3.4.1.2 Inference in a Bayesian Network
3.4.1.3 Learning Bayesian Networks
3.4.2 Hidden Markov Models
3.4.2.1 The Inference and Learning Algorithms
3.4.3 Markov Random Fields
3.4.3.1 Conditional Independence
3.4.3.2 Clique Factorization
3.4.3.3 The Inference and Learning Algorithms
3.4.4 Conditional Random Fields
3.4.4.1 The Learning Algorithms
3.5 Summary
Bibliography
Figure 3.1
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Chapter 4 Decision Trees: Theory and Algorithms
4.1 Introduction
4.2 Top-Down Decision Tree Induction
4.2.1 Node Splitting
4.2.2 Tree Pruning
4.3 Case Studies with C4.5 and CART
4.3.1 Splitting Criteria
4.3.2 Stopping Conditions
4.3.3 Pruning Strategy
4.3.4 Handling Unknown Values: Induction and Prediction
4.3.5 Other Issues: Windowing and Multivariate Criteria
4.4 Scalable Decision Tree Construction
4.4.1 RainForest-Based Approach
4.4.2 SPIES Approach
4.4.3 Parallel Decision Tree Construction
4.5 Incremental Decision Tree Induction
4.5.1 ID3 Family
4.5.2 VFDT Family
4.5.3 Ensemble Method for Streaming Data
4.6 Summary
Bibliography
Figure 4.1
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Table 4.1
Table 4.1
Table 4.2
Table 4.3
Chapter 5 Rule-Based Classification
5.1 Introduction
5.2 Rule Induction
5.2.1 Two Algorithms for Rule Induction
5.2.1.1 CN2 Induction Algorithm (Ordered Rules)
5.2.1.2 RIPPER Algorithm and Its Variations (Ordered Classes)
5.2.2 Learn One Rule in Rule Learning
5.3 Classification Based on Association Rule Mining
5.3.1 Association Rule Mining
5.3.1.1 Definitions of Association Rules, Support, and Confidence
5.3.1.2 The Introduction of Apriori Algorithm
5.3.2 Mining Class Association Rules
5.3.3 Classification Based on Associations
5.3.3.1 Additional Discussion for CARs Mining
5.3.3.2 Building a Classifier Using CARs
5.3.4 Other Techniques for Association Rule-Based Classification
5.4 Applications
5.4.1 Text Categorization
5.4.2 Intrusion Detection
5.4.3 Using Class Association Rules for Diagnostic Data Mining
5.4.4 Gene Expression Data Analysis
5.5 Discussion and Conclusion
Bibliography
Table 5.1
Table 5.1
Table 5.2
Table 5.3
Chapter 6 Instance-Based Learning: A Survey
6.1 Introduction
6.2 Instance-Based Learning Framework
6.3 The Nearest Neighbor Classifier
6.3.1 Handling Symbolic Attributes
6.3.2 Distance-Weighted Nearest Neighbor Methods
6.3.3 Local Distance Scaling
6.3.4 Attribute-Weighted Nearest Neighbor Methods
6.3.5 Locally Adaptive Nearest Neighbor Classifier
6.3.6 Combining with Ensemble Methods
6.3.7 Multi-Label Learning
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
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Chapter 7 Support Vector Machines
7.1 Introduction
7.2 The Maximum Margin Perspective
7.3 The Regularization Perspective
7.4 The Support Vector Perspective
7.5 Kernel Tricks
7.6 Solvers and Algorithms
7.7 Multiclass Strategies
7.8 Conclusion
Bibliography
Figure 7.1
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 7.5
Figure 7.6
Figure 7.7
Figure 7.8
Figure 7.9
Chapter 8 Neural Networks: A Review
8.1 Introduction
8.2 Fundamental Concepts
8.2.1 Mathematical Model of a Neuron
8.2.2 Types of Units
8.2.2.1 McCullough Pitts Binary Threshold Unit
8.2.2.2 Linear Unit
8.2.2.3 Linear Threshold Unit
8.2.2.4 Sigmoidal Unit
8.2.2.5 Distance Unit
8.2.2.6 Radial Basis Unit
8.2.2.7 Polynomial Unit
8.2.3 Network Topology
8.2.3.1 Layered Network
8.2.3.2 Networks with Feedback
8.2.3.3 Modular Networks
8.2.4 Computation and Knowledge Representation
8.2.5 Learning
8.2.5.1 Hebbian Rule
8.2.5.2 The Delta Rule
8.3 Single-Layer Neural Network
8.3.1 The Single-Layer Perceptron
8.3.1.1 Perceptron Criterion
8.3.1.2 Multi-Class Perceptrons
8.3.1.3 Perceptron Enhancements
8.3.2 Adaline
8.3.2.1 Two-Class Adaline
8.3.2.2 Multi-Class Adaline
8.3.3 Learning Vector Quantization (LVQ)
8.3.3.1 LVQ1 Training
8.3.3.2 LVQ2 Training
8.3.3.3 Application and Limitations
8.4 Kernel Neural Network
8.4.1 Radial Basis Function Network
8.4.2 RBFN Training
8.4.2.1 Using Training Samples as Centers
8.4.2.2 Random Selection of Centers
8.4.2.3 Unsupervised Selection of Centers
8.4.2.4 Supervised Estimation of Centers
8.4.2.5 Linear Optimization of Weights
8.4.2.6 Gradient Descent and Enhancements
8.4.3 RBF Applications
8.5 Multi-Layer Feedforward Network
8.5.1 MLP Architecture for Classification
8.5.1.1 Two-Class Problems
8.5.1.2 Multi-Class Problems
8.5.1.3 Forward Propagation
8.5.2 Error Metrics
8.5.2.1 Mean Square Error (MSE)
8.5.2.2 Cross-Entropy (CE)
8.5.2.3 Minimum Classification Error (MCE)
8.5.3 Learning by Backpropagation
8.5.4 Enhancing Backpropagation
8.5.4.1 Backpropagation with Momentum
8.5.4.2 Delta-Bar-Delta
8.5.4.3 Rprop Algorithm
8.5.4.4 Quick-Prop
8.5.5 Generalization Issues
8.5.6 Model Selection
8.6 Deep Neural Networks
8.6.1 Use of Prior Knowledge
8.6.2 Layer-Wise Greedy Training
8.6.2.1 Deep Belief Networks (DBNs)
8.6.2.2 Stack Auto-Encoder
8.6.3 Limits and Applications
8.7 Summary
Acknowledgements
Bibliography
Figure 8.1
Figure 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Table 8.1
Table 8.1
Chapter 9 A Survey of Stream Classification Algorithms
9.1 Introduction
9.2 Generic Stream Classification Algorithms
9.2.1 Decision Trees for Data Streams
9.2.2 Rule-Based Methods for Data Streams
9.2.3 Nearest Neighbor Methods for Data Streams
9.2.4 SVM Methods for Data Streams
9.2.5 Neural Network Classifiers for Data Streams
9.2.6 Ensemble Methods for Data Streams
9.3 Rare Class Stream Classification
9.3.1 Detecting Rare Classes
9.3.2 Detecting Novel Classes
9.3.3 Detecting Infrequently Recurring Classes
9.4 Discrete Attributes: The Massive Domain Scenario
9.5 Other Data Domains
9.5.1 Text Streams
9.5.2 Graph Streams
9.5.3 Uncertain Data Streams
9.6 Conclusions and Summary
Bibliography
Figure 9.1
Figure 9.1
Figure 9.2
Table 9.1
Table 9.1
Chapter 10 Big Data Classification
10.1 Introduction
10.2 Scale-Up on a Single Machine
10.2.1 Background
10.2.2 SVMPerf
10.2.3 Pegasos
10.2.4 Bundle Methods
10.3 Scale-Up by Parallelism
10.3.1 Parallel Decision Trees
10.3.2 Parallel SVMs
10.3.3 MRM-ML
10.3.4 SystemML
10.4 Conclusion
Bibliography
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
11.5.1 Bernoulli Multivariate Model
11.5.2 Multinomial Distribution
11.5.3 Mixture Modeling for Text Classification
11.6 Linear Classifiers
11.6.1 SVM Classifiers
11.6.2 Regression-Based Classifiers
11.6.3 Neural Network Classifiers
11.6.4 Some Observations about Linear Classifiers
11.7 Proximity-Based Classifiers
11.8 Classification of Linked and Web Data
11.9 Meta-Algorithms for Text Classification
11.9.1 Classifier Ensemble Learning
11.9.2 Data Centered Methods: Boosting and Bagging
11.9.3 Optimizing Specific Measures of Accuracy
11.10 Leveraging Additional Training Data
11.10.1 Semi-Supervised Learning
11.10.2 Transfer Learning
11.10.3 Active Learning
11.11 Conclusions and Summary
Bibliography
Figure 11.1
Figure 11.1
Figure 11.2
Figure 11.3
Figure 11.4
Chapter 12 Multimedia Classification
12.1 Introduction
12.1.1 Overview
12.2 Feature Extraction and Data Pre-Processing
12.2.1 Text Features
12.2.2 Image Features
12.2.3 Audio Features
12.2.4 Video Features
12.3 Audio Visual Fusion
12.3.1 Fusion Methods
12.3.2 Audio Visual Speech Recognition
12.3.2.1 Visual Front End
12.3.2.2 Decision Fusion on HMM
12.3.3 Other Applications
12.4 Ontology-Based Classification and Inference
12.4.1 Popular Applied Ontology
12.4.2 Ontological Relations
12.4.2.1 Definition
12.4.2.2 Subclass Relation
12.4.2.3 Co-Occurrence Relation
12.4.2.4 Combination of the Two Relations
12.4.2.5 Inherently Used Ontology
12.5 Geographical Classification with Multimedia Data
12.5.1 Data Modalities
12.5.2 Challenges in Geographical Classification
12.5.3 Geo-Classification for Images
12.5.3.1 Classifiers
12.5.4 Geo-Classification for Web Videos
12.6 Conclusion
Bibliography
Figure 12.1
Figure 12.1
Figure 12.2
Figure 12.3
Figure 12.4
Figure 12.5
Figure 12.6
Figure 12.7
Figure 12.8
Figure 12.9
Chapter 13 Time Series Data Classification
13.1 Introduction
13.2 Time Series Representation
13.3 Distance Measures
13.3.1 Lp-Norms
13.3.2 Dynamic Time Warping (DTW)
13.3.3 Edit Distance
13.3.4 Longest Common Subsequence (LCSS)
13.4 k-NN
13.4.1 Speeding up the k-NN
13.5 Support Vector Machines (SVMs)
13.6 Classification Trees
13.7 Model-Based Classification
13.8 Distributed Time Series Classification
13.9 Conclusion
Acknowledgements
Bibliography
Chapter 14 Discrete Sequence Classification
14.1 Introduction
14.2 Background
14.2.1 Sequence
14.2.2 Sequence Classification
14.2.3 Frequent Sequential Patterns
14.2.4 n-Grams
14.3 Sequence Classification Methods
14.4 Feature-Based Classification
14.4.1 Filtering Method for Sequential Feature Selection
14.4.2 Pattern Mining Framework for Mining Sequential Features
14.4.3 A Wrapper-Based Method for Mining Sequential Features
14.5 Distance-Based Methods
14.5.0.1 Alignment-Based Distance
14.5.0.1 Alignment-Based Distance
14.5.0.2 Keyword-Based Distance
14.5.0.3 Kernel-Based Similarity
14.5.0.4 Model-Based Similarity
14.5.0.5 Time Series Distance Metrics
14.6 Model-Based Method
14.7 Hybrid Methods
14.8 Non-Traditional Sequence Classification
14.8.1 Semi-Supervised Sequence Classification
14.8.2 Classification of Label Sequences
14.8.3 Classification of Sequence of Vector Data
14.9 Conclusions
Bibliography
Chapter 15 Collective Classification of Network Data
15.1 Introduction
15.2 Collective Classification Problem Definition
15.2.1 Inductive vs. Transductive Learning
15.2.2 Active Collective Classification
15.3 Iterative Methods
15.3.1 Label Propagation
15.3.2 Iterative Classification Algorithms
15.4 Graph-Based Regularization
15.5 Probabilistic Graphical Models
15.5.1 Directed Models
15.5.2 Undirected Models
15.5.3 Approximate Inference in Graphical Models
15.5.3.1 Gibbs Sampling
15.5.3.2 Loopy Belief Propagation (LBP)
15.6 Feature Construction
15.6.1 Data Graph
15.6.2 Relational Features
15.7 Applications of Collective Classification
15.8 Conclusion
Acknowledgements
Bibliography
Figure 15.1
Figure 15.1
Figure 15.2
Chapter 16 Uncertain Data Classification
16.1 Introduction
16.2 Preliminaries
16.2.1 Data Uncertainty Models
16.2.2 Classification Framework
16.3 Classification Algorithms
16.3.1 Decision Trees
16.3.2 Rule-Based Classification
16.3.3 Associative Classification
16.3.4 Density-Based Classification
16.3.5 Nearest Neighbor-Based Classification
16.3.6 Support Vector Classification
16.3.7 Naive Bayes Classification
16.4 Conclusions
Bibliography
Figure 16.1
Figure 16.1
Figure 16.2
Figure 16.3
Figure 16.4
Figure 16.5
Chapter 17 Rare Class Learning
17.1 Introduction
17.2 Rare Class Detection
17.2.1 Cost Sensitive Learning
17.2.1.1 MetaCost: A Relabeling Approach
17.2.1.2 Weighting Methods
17.2.1.3 Bayes Classifiers
17.2.1.4 Proximity-Based Classifiers
17.2.1.5 Rule-Based Classifiers
17.2.1.6 Decision Trees
17.2.1.7 SVM Classifier
17.2.2 Adaptive Re-Sampling
17.2.2.1 Relation between Weighting and Sampling
17.2.2.2 Synthetic Over-Sampling: SMOTE
17.2.2.3 One Class Learning with Positive Class
17.2.2.4 Ensemble Techniques
17.2.3 Boosting Methods
17.3 The Semi-Supervised Scenario: Positive and Unlabeled Data
17.3.1 Difficult Cases and One-Class Learning
17.4 The Semi-Supervised Scenario: Novel Class Detection
17.4.1 One Class Novelty Detection
17.4.2 Combining Novel Class Detection with Rare Class Detection
17.4.3 Online Novelty Detection
17.5 Human Supervision
17.6 Other Work
17.7 Conclusions and Summary
Bibliography
Figure 17.1
Figure 17.1
Figure 17.2
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
18.4.1 Semi-Supervised Metric Learning
18.4.1.1 Laplacian Regularized Metric Learning (LRML)
18.4.1.2 Constraint Margin Maximization (CMM)
18.4.2 Online Learning
18.4.2.1 Pseudo-Metric Online Learning Algorithm (POLA)
18.4.2.2 Online Information Theoretic Metric Learning (OITML)
18.5 Conclusions and Discussions
Bibliography
Table 18.1
Table 18.1
Table 18.2
Chapter 19 Ensemble Learning
19.1 Introduction
19.2 Bayesian Methods
19.2.1 Bayes Optimal Classifier
19.2.2 Bayesian Model Averaging
19.2.3 Bayesian Model Combination
19.3 Bagging
19.3.1 General Idea
19.3.2 Random Forest
19.4 Boosting
19.4.1 General Boosting Procedure
19.4.2 AdaBoost
19.5 Stacking
19.5.1 General Stacking Procedure
19.5.2 Stacking and Cross-Validation
19.5.3 Discussions
19.6 Recent Advances in Ensemble Learning
19.7 Conclusions
Bibliography
Figure 19.1
Figure 19.1
Table 19.1
Table 19.1
Table 19.2
Table 19.3
Table 19.4
Table 19.5
Table 19.6
Table 19.7
Table 19.8
Table 19.9
Table 19.10
Table 19.11
Table 19.12
Table 19.13
Table 19.14
Table 19.15
Chapter 20 Semi-Supervised Learning
20.1 Introduction
20.1.1 Transductive vs. Inductive Semi-Supervised Learning
20.1.2 Semi-Supervised Learning Framework and Assumptions
20.2 Generative Models
20.2.1 Algorithms
20.2.2 Description of a Representative Algorithm
20.2.3 Theoretical Justification and Relevant Results
20.3 Co-Training
20.3.1 Algorithms
20.3.2 Description of a Representative Algorithm
20.3.3 Theoretical Justification and Relevant Results
20.4 Graph-Based Methods
20.4.1 Algorithms
20.4.1.1 Graph Cut
20.4.1.2 Graph Transduction
20.4.1.3 Manifold Regularization
20.4.1.4 Random Walk
20.4.1.5 Large Scale Learning
20.4.2 Description of a Representative Algorithm
20.4.3 Theoretical Justification and Relevant Results
20.5 Semi-Supervised Learning Methods Based on Cluster Assumption
20.5.1 Algorithms
20.5.2 Description of a Representative Algorithm
20.5.3 Theoretical Justification and Relevant Results
20.6 Related Areas
20.7 Concluding Remarks
Bibliography
Figure 20.1
Figure 20.1
Figure 20.2
Chapter 21 Transfer Learning
21.1 Introduction
21.2 Transfer Learning Overview
21.2.1 Background
21.2.2 Notations and Definitions
21.3 Homogenous Transfer Learning
21.3.1 Instance-Based Approach
21.3.1.1 Case I: No Target Labeled Data
21.3.1.2 Case II: A Few Target Labeled Data
21.3.2 Feature-Representation-Based Approach
21.3.2.1 Encoding Specific Knowledge for Feature Learning
21.3.2.2 Learning Features by Minimizing Distance between Distributions
21.3.2.3 Learning Features Inspired by Multi-Task Learning
21.3.2.4 Learning Features Inspired by Self-Taught Learning
21.3.2.5 Other Feature Learning Approaches
21.3.3 Model-Parameter-Based Approach
21.3.4 Relational-Information-Based Approaches
21.4 Heterogeneous Transfer Learning
21.4.1 Heterogeneous Feature Spaces
21.4.2 Different Label Spaces
21.5 Transfer Bounds and Negative Transfer
21.6 Other Research Issues
21.6.1 Binary Classification vs. Multi-Class Classification
21.6.2 Knowledge Transfer from Multiple Source Domains
21.6.3 Transfer Learning Meets Active Learning
21.7 Applications of Transfer Learning
21.7.1 NLP Applications
21.7.2 Web-Based Applications
21.7.3 Sensor-Based Applications
21.7.4 Applications to Computer Vision
21.7.5 Applications to Bioinformatics
21.7.6 Other Applications
21.8 Concluding Remarks
Bibliography
Figure 21.1
Figure 21.1
Table 21.1
Table 21.1
Table 21.2
Table 21.3
Chapter 22 Active Learning: A Survey
22.1 Introduction
22.2 Motivation and Comparisons to Other Strategies
22.2.1 Comparison with Other Forms of Human Feedback
22.2.2 Comparisons with Semi-Supervised and Transfer Learning
22.3 Querying Strategies
22.3.1 Heterogeneity-Based Models
22.3.1.1 Uncertainty Sampling
22.3.1.2 Query-by-Committee
22.3.1.3 Expected Model Change
22.3.2 Performance-Based Models
22.3.2.1 Expected Error Reduction
22.3.2.2 Expected Variance Reduction
22.3.3 Representativeness-Based Models
22.3.4 Hybrid Models
22.4 Active Learning with Theoretical Guarantees
22.4.1 A Simple Example
22.4.2 Existing Works
22.4.3 Preliminaries
22.4.4 Importance Weighted Active Learning
22.4.4.1 Algorithm
22.4.4.2 Consistency
22.4.4.3 Label Complexity
22.5 Dependency-Oriented Data Types for Active Learning
22.5.1 Active Learning in Sequences
22.5.2 Active Learning in Graphs
22.5.2.1 Classification of Many Small Graphs
22.5.2.2 Node Classification in a Single Large Graph
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
Figure 22.1
Figure 22.2
Chapter 23 Visual Classification
23.1 Introduction
23.1.1 Requirements for Visual Classification
23.1.2 Visualization Metaphors
23.1.2.1 2D and 3D Spaces
23.1.2.2 More Complex Metaphors
23.1.3 Challenges in Visual Classification
23.1.4 Related Works
23.2 Approaches
23.2.1 Nomograms
23.2.1.1 Naïve Bayes Nomogram
23.2.2 Parallel Coordinates
23.2.2.1 Edge Cluttering
23.2.3 Radial Visualizations
23.2.3.1 Star Coordinates
23.2.4 Scatter Plots
23.2.4.1 Clustering
23.2.4.2 Naïve Bayes Classification
23.2.5 Topological Maps
23.2.5.1 Self-Organizing Maps
23.2.5.2 Generative Topographic Mapping
23.2.6 Trees
23.2.6.1 Decision Trees
23.2.6.2 Treemap
23.2.6.3 Hyperbolic Tree
23.2.6.4 Phylogenetic Trees
23.3 Systems
23.3.1 EnsembleMatrix and ManiMatrix
23.3.2 Systematic Mapping
23.3.3 iVisClassifier
23.3.4 ParallelTopics
23.3.5 VisBricks
23.3.6 WHIDE
23.3.7 Text Document Retrieval
23.4 Summary and Conclusions
Bibliography
Figure 23.1
Figure 23.1
Figure 23.2
Figure 23.3
Figure 23.4
Figure 23.5
Figure 23.6
Figure 23.7
Chapter 24 Evaluation of Classification Methods
24.1 Introduction
24.2 Validation Schemes
24.3 Evaluation Measures
24.3.1 Accuracy Related Measures
24.3.1.1 Discrete Classifiers
24.3.1.2 Probabilistic Classifiers
24.3.2 Additional Measures
24.4 Comparing Classifiers
24.4.1 Parametric Statistical Comparisons
24.4.1.1 Pairwise Comparisons
24.4.1.2 Multiple Comparisons
24.4.2 Non-Parametric Statistical Comparisons
24.4.2.1 Pairwise Comparisons
24.4.2.2 Multiple Comparisons
24.4.2.3 Permutation Tests
24.5 Concluding Remarks
Bibliography
Figure 24.1
Figure 24.1
Figure 24.2
Figure 24.3
Figure 24.4
Table 24.1
Table 24.1
Table 24.2
Table 24.3
Table 24.4
Table 24.5
Chapter 25 Educational and Software Resources for Data Classification
25.1 Introduction
25.2 Educational Resources
25.2.1 Books on Data Classification
25.2.2 Popular Survey Papers on Data Classification
25.3 Software for Data Classification
25.3.1 Data Benchmarks for Software and Research
25.4 Summary
Bibliography
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Preliminaries
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