Ensemble methods : foundations and algorithms /: foundations and algorithms. ([2012])

• Record Type: Book
• Further Information: Note: Zhi-Hua Zhou.
• Authors: (Computer scientist), Zhou, Zhi-Hua
• Contents: 1. Introduction -- 1.1. Basic Concepts -- 1.2. Popular Learning Algorithms -- 1.2.1. Linear Discriminant Analysis -- 1.2.2. Decision Trees -- 1.2.3. Neural Networks -- 1.2.4. Naive Bayes Classifier -- 1.2.5.k-Nearest Neighbor -- 1.2.6. Support Vector Machines and Kernel Methods -- 1.3. Evaluation and Comparison -- 1.4. Ensemble Methods -- 1.5. Applications of Ensemble Methods -- 1.6. Further Readings -- 2. Boosting -- 2.1.A General Boosting Procedure -- 2.2. The AdaBoost Algorithm -- 2.3. Illustrative Examples -- 2.4. Theoretical Issues -- 2.4.1. Initial Analysis -- 2.4.2. Margin Explanation -- 2.4.3. Statistical View -- 2.5. Multiclass Extension -- 2.6. Noise Tolerance -- 2.7. Further Readings -- 3. Bagging -- 3.1. Two Ensemble Paradigms -- 3.2. The Bagging Algorithm -- 3.3. Illustrative Examples -- 3.4. Theoretical Issues -- 3.5. Random Tree Ensembles -- 3.5.1. Random Forest -- 3.5.2. Spectrum of Randomization -- 3.5.3. Random Tree Ensembles for Density Estimation -- 3.5.4. Random Tree Ensembles for Anomaly Detection -- 3.6. Further Readings -- 4.Combination Methods -- 4.1. Benefits of Combination -- 4.2. Averaging -- 4.2.1. Simple Averaging -- 4.2.2. Weighted Averaging -- 4.3. Voting -- 4.3.1. Majority Voting -- 4.3.2. Plurality Voting -- 4.3.3. Weighted Voting -- 4.3.4. Soft Voting -- 4.3.5. Theoretical Issues -- 4.4.Combining by Learning -- 4.4.1. Stacking -- 4.4.2. Infinite Ensemble -- 4.5. Other Combination Methods -- 4.5.1. Algebraic Methods -- 4.5.2. Behavior Knowledge Space Method -- 4.5.3. Decision Template Method -- 4.6. Relevant Methods -- 4.6.1. Error-Correcting Output Codes -- 4.6.2. Dynamic Classifier Selection -- 4.6.3. Mixture of Experts -- 4.7. Further Readings -- 5. Diversity -- 5.1. Ensemble Diversity -- 5.2. Error Decomposition -- 5.2.1. Error-Ambiguity Decomposition -- 5.2.2. Bias-Variance-Covariance Decomposition -- 5.3. Diversity Measures -- 5.3.1. Pairwise Measures -- 5.3.2. Non-Pairwise Measures -- 5.3.3. Summary and Visualization -- 5.3.4. Limitation of Diversity Measures -- 5.4. Information Theoretic Diversity -- 5.4.1. Information Theory and Ensemble -- 5.4.2. Interaction Information Diversity -- 5.4.3. Multi-Information Diversity -- 5.4.4. Estimation Method -- 5.5. Diversity Generation -- 5.6. Further Readings -- 6. Ensemble Pruning -- 6.1. What Is Ensemble Pruning -- 6.2. Many Could Be Better Than All -- 6.3. Categorization of Pruning Methods -- 6.4. Ordering-Based Pruning -- 6.5. Clustering-Based Pruning -- 6.6. Optimization-Based Pruning -- 6.6.1. Heuristic Optimization Pruning -- 6.6.2. Mathematical Programming Pruning -- 6.6.3. Probabilistic Pruning -- 6.7. Further Readings -- 7. Clustering Ensembles -- 7.1. Clustering -- 7.1.1. Clustering Methods -- 7.1.2. Clustering Evaluation -- 7.1.3. Why Clustering Ensembles -- 7.2. Categorization of Clustering Ensemble Methods -- 7.3. Similarity-Based Methods -- 7.4. Graph-Based Methods -- 7.5. Relabeling-Based Methods -- 7.6. Transformation-Based Methods -- 7.7. Further Readings -- 8. Advanced Topics -- 8.1. Semi-Supervised Learning -- 8.1.1. Usefulness of Unlabeled Data -- 8.1.2. Semi-Supervised Learning with Ensembles -- 8.2. Active Learning -- 8.2.1. Usefulness of Human Intervention -- 8.2.2. Active Learning with Ensembles -- 8.3. Cost-Sensitive Learning -- 8.3.1. Learning with Unequal Costs -- 8.3.2. Ensemble Methods for Cost-Sensitive Learning -- 8.4. Class-Imbalance Learning -- 8.4.1. Learning with Class Imbalance -- 8.4.2. Performance Evaluation with Class Imbalance -- 8.4.3. Ensemble Methods for Class-Imbalance Learning -- 8.5. Improving Comprehensibility -- 8.5.1. Reduction of Ensemble to Single Model -- 8.5.2. Rule Extraction from Ensembles -- 8.5.3. Visualization of Ensembles -- 8.6. Future Directions of Ensembles -- 8.7. Further Readings.
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2012
• Copyright Date: 2012
• Extent: 1 online resource (xiv, 222 pages), illustrations
• Subjects: 519.5/35; Multiple comparisons (Statistics); Set theory; Mathematical analysis; Data Interpretation, Statistical; Statistics as Topic; BUSINESS & ECONOMICS -- Statistics; COMPUTERS -- Database Management -- Data Mining; COMPUTERS -- Machine Theory; MATHEMATICS -- Probability & Statistics -- Multivariate Analysis; Mathematical analysis; Multiple comparisons (Statistics); Set theory; Electronic books
• Languages: English
• ISBNs: 9781439830055; 1439830053; 9781306499743; 1306499747
• Related ISBNs: 9781439830031; 1439830037
• Notes: Note: Includes bibliographical references (pages 187-218) and index.; Note: Print version record.
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