Ensemble methods: foundations and algorithms
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla. [u.a.]
Chapman & Hall/CRC
2012
|
| Series: | Chapman & Hall/CRC Machine learning & pattern recognition series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 187 - 218 |
| Physical Description: | XIV, 222 Seiten Diagramme |
| ISBN: | 9781439830031 |
Staff View
MARC
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| 300 | |a XIV, 222 Seiten |b Diagramme | ||
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| 500 | |a Literaturverz. S. 187 - 218 | ||
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Record in the Search Index
| _version_ | 1804149509929828352 |
|---|---|
| adam_text | Contents
Preface
vii
Notations
¡x
1
Introduction
1
1.1
Basic Concepts
........................... 1
1.2
Popular
Learning Algorithms
..................
З
1.2.1
Linear Discriminant Analysis
.............. 3
1.2.2
Decision Trees
....................... 4
1.2.3
Neural Networks
..................... 6
1.2.4
Naïve Bayes
Classifier
................... 8
1.2.5
fe-Nearest Neighbor
.................... 9
1.2.6
Support Vector Machines and Kernel Methods
.... 9
1.3
Evaluation and Comparison
................... 12
1.4
Ensemble Methods
....................... . 15
1.5
Applications of Ensemble Methods
............... 17
1.6
Further Readings
......................... 20
2
Boosting
23
2.1
A General Boosting Procedure
.................. 23
2.2
The AdaBoost Algorithm
..................... 24
2.3
Illustrative Examples
....................... 28
2.4
Theoretical Issues
......................... 32
2.4.1
Initial Analysis
....................... 32
2.4.2
Margin Explanation
.................... 32
2.4.3
Statistical View
...................... 35
2.5
Multiclass Extension
....................... 38
2.6
Noise Tolerance
.......................... 41
2.7
Further Readings
......................... 44
3
Bagging
47
3.1
Two Ensemble Paradigms
.................... 47
3.2
The Bagging Algorithm
.......-.............. 48
3.3
Illustrative Examples
....................... 50
3.4
Theoretical Issues
..............·.......... 53
3.5
Random Tree Ensembles
..................... 57
3.5Л
Random Forest
...................... 57
xii Contents
3.5.2 Spectram
of Randomization...............
59
3.5.3
Random Tree
Ensembles
for Density Estimation
... 61
3.5.4
Random Tree Ensembles for Anomaly Detection
... 64
3.6
Further Readings
......................... 66
4
Combination Methods
67
4.1
Benefits of Combination
..................... 67
4.2
Averaging
.............................. 68
4.2.1
Simple Averaging
..................... 68
4.2.2
Weighted Averaging
.................... 70
4.3
Voting
................................ 71
4.3.1
MajorityVoting
...................... 72
4.3.2
Plurality Voting
...................... 73
4.3.3
Weighted Voting
...................... 74
4.3.4
SoftVoting
......................... 75
4.3.5
Theoretical Issues
..................... 77
4.4
Combining by Learning
...................... 83
4.4.1
Stacking
.......................... 83
4.4.2
Infinite Ensemble
..................... 86
4.5
Other Combination Methods
.................. 87
4.5.1
Algebraic Methods
.................... 87
4.5.2
Behavior Knowledge Space Method.
.......... 88
4.5.3
Decision Template Method
............... 89
4.6
Relevant Methods
......................... 89
4.6.1
Error-Correcting Output Codes
............. 90
4.6.2
Dynamic Classifier Selection
.............. 93
4.6.3
Mixture of Experts
..................... 93
4.7
Further Readings
......................... 95
5
Diversity
99
5.1
Ensemble Diversity
........................ 99
5.2
Error Decomposition
....................... 100
5.2.1
Error-Ambiguity Decomposition
............ 100
5.2.2
Bias-Variance-Covariance Decomposition
....... 102
5.3
Diversity Measures
........................ 105
5.3.1
Pairwise Measures
.................... 105
5.3.2
Non-Pairwise Measures
................. 106
5.3.3
Summary and Visualization
............... 109
5.3.4
Limitation of Diversity Measures
............ 110
5.4
Information Theoretic Diversity
.................
Ill
5.4.1
Information Theory and Ensemble
...........
Ш
5.4.2
Interaction Information Diversity
............ 112
5.4
J
Multi-Information Diversity
............... 113
5.4.4
Estimation Method
.................... 114
5.5
Diversity Generation
....................... 116
Contents xiii
5.6
Further Readings
.........................
П8
6
Ensemble Pruning
119
6.1
What Is Ensemble Pruning
.................... 119
6.2
Many Could Be Better Than All
................. 120
6.3
Categorization of Pruning Methods
............... 123
6.4
Ordering-Based Pruning
..................... 124
6.5
Clustering-Based Pruning
.................... 127
6.6
Optimization-Based Pruning
................... 128
6.6.1
Heuristic Optimization Pruning
............. 128
6.6.2
Mathematical Programming Pruning
.......... 129
6.6.3
Probabilistic Pruning
................... 131
6.7
Further Readings
......................... 133
7
Clustering Ensembles
135
7.1
Clustering
............................. 135
7.1.1
Clustering Methods
.................... 135
7.1.2
Clustering Evaluation
................... 137
7.1.3
Why Clustering Ensembles
................ 139
7.2
Categorization of Clustering Ensemble Methods
....... 141
7.3
Similarity-Based Methods
.................... 142
7.4
Graph-Based Methods
...................... 144
7.5
Relabeling-Based Methods
.................... 147
7.6
Transformation-Based Methods
................. 152
7.7
Further Readings
......................... 155
8
Advanced Topics
157
8.1
Semi-Supervised Learning
.................... 157
8.1.1
Usefulness of Unlabeled Data
.............. 157
8.1.2
Semi-Supervised Learning with Ensembles
...... 159
8.2
ActiveLearning
.......................... 163
8.2.1
Usefulness of Human Intervention
........... 163
8.2.2
Active Learning with Ensembles
............ 165
8.3
Cost-Sensitive Learning
...................... 166
8.3.1
Learning with Unequal Costs
.............. 166
8.3.2
Ensemble Methods for Cost-Sensitive Learning
. . . . 167
8.4
Class-Imbalance Learning
.................... 171
8.4.1
Learning with Class Imbalance
............. 171
8.4.2
Performance Evaluation with Class Imbalance
.... 172
8.4.3
Ensemble Methods for Class-Imbalance Learning
. . 176
8.5
Improving Comprehensibfflty
.................. 179
8.5.1
Reduction of Ensemble to Single Model
........ 179
8.5.2
Rule Extraction from Ensembles
............ 180
8.5.3
Visualization of Ensembles
............... 181
8.6
Future Directions of Ensembles
................. 182
xiv Contents
8.7
Further Readings
......................... 184
References
187
Index
219
|
| any_adam_object | 1 |
| author | Zhou, Zhi-Hua |
| author_GND | (DE-588)133174131 |
| author_facet | Zhou, Zhi-Hua |
| author_role | aut |
| author_sort | Zhou, Zhi-Hua |
| author_variant | z h z zhz |
| building | Verbundindex |
| bvnumber | BV040449607 |
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| ctrlnum | (OCoLC)815939865 (DE-599)HBZHT017135594 |
| discipline | Informatik |
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| id | DE-604.BV040449607 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:24:10Z |
| institution | BVB |
| isbn | 9781439830031 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025297292 |
| oclc_num | 815939865 |
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| owner | DE-473 DE-BY-UBG DE-29 DE-523 DE-945 DE-703 |
| owner_facet | DE-473 DE-BY-UBG DE-29 DE-523 DE-945 DE-703 |
| physical | XIV, 222 Seiten Diagramme |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Chapman & Hall/CRC Machine learning & pattern recognition series |
| spelling | Zhou, Zhi-Hua Verfasser (DE-588)133174131 aut Ensemble methods foundations and algorithms Zhi-Hua Zhou Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2012 XIV, 222 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC Machine learning & pattern recognition series Literaturverz. S. 187 - 218 Stochastisches Modell (DE-588)4057633-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Stochastisches Modell (DE-588)4057633-4 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4398-3005-5 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297292&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhou, Zhi-Hua Ensemble methods foundations and algorithms Stochastisches Modell (DE-588)4057633-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4057633-4 (DE-588)4193754-5 |
| title | Ensemble methods foundations and algorithms |
| title_auth | Ensemble methods foundations and algorithms |
| title_exact_search | Ensemble methods foundations and algorithms |
| title_full | Ensemble methods foundations and algorithms Zhi-Hua Zhou |
| title_fullStr | Ensemble methods foundations and algorithms Zhi-Hua Zhou |
| title_full_unstemmed | Ensemble methods foundations and algorithms Zhi-Hua Zhou |
| title_short | Ensemble methods |
| title_sort | ensemble methods foundations and algorithms |
| title_sub | foundations and algorithms |
| topic | Stochastisches Modell (DE-588)4057633-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Stochastisches Modell Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025297292&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zhouzhihua ensemblemethodsfoundationsandalgorithms |