Data mining with decision trees: theory and applications
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New Jersey [u.a.]
World Scientific
[2015]
|
| Edition: | 2nd Edition |
| Series: | Series in machine perception and artificial intelligence
81 |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027630832&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | xxi, 305 Seiten Illustrationen |
| ISBN: | 9789814590075 |
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Record in the Search Index
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| adam_text | Titel: Data mining with decision trees
Autor: Rokach, Lior
Jahr: 2015
Contents
About the Authors vi
Preface for the Second Edition vii
Preface for the First Edition ix
1. Introduction to Decision Trees 1
1.1 Data Science 1
1.2 Data Mining 2
1.3 The Four-Layer Model 3
1.4 Knowledge Discovery in Databases (KDD) 4
1.5 Taxonomy of Data Mining Methods 8
1.6 Supervised Methods 9
1.6.1 Overview 9
1.7 Classification Trees 10
1.8 Characteristics of Classification Trees 12
1.8.1 Tree Size 14
1.8.2 The Hierarchical Nature of Decision Trees .... 15
1.9 Relation to Rule Induction 15
2. Training Decision Trees 17
2.1 What is Learning? 17
2.2 Preparing the Training Set 17
2.3 Training the Decision Tree 19
xiii
xiv Data Mining with Decision Trees
3. A Generic Algorithm for Top-Down Induction
of Decision Trees 23
3.1 Training Set 23
3.2 Definition of the Classification Problem 25
3.3 Induction Algorithms 26
3.4 Probability Estimation in Decision Trees 26
3.4.1 Laplace Correction 27
3.4.2 No Match 28
3.5 Algorithmic Framework for Decision Trees 28
3.6 Stopping Criteria 30
4. Evaluation of Classification Trees 31
4.1 Overview 31
4.2 Generalization Error 31
4.2.1 Theoretical Estimation of Generalization
Error 32
4.2.2 Empirical Estimation of Generalization
Error 32
4.2.3 Alternatives to the Accuracy Measure 34
4.2.4 The F-Measure 35
4.2.5 Confusion Matrix 36
4.2.6 Classifier Evaluation under Limited
Resources 37
4.2.6.1 ROC Curves 39
4.2.6.2 Hit-Rate Curve 40
4.2.6.3 Qrecall (Quota Recall) 40
4.2.6.4 Lift Curve 41
4.2.6.5 Pearson Correlation Coefficient 41
4.2.6.6 Area Under Curve (AUC) 43
4.2.6.7 Average Hit-Rate 44
4.2.6.8 Average Qrecall 44
4.2.6.9 Potential Extract Measure (PEM) .... 45
4.2.7 Which Decision Tree Classifier is Better? 48
4.2.7.1 McNemar s Test 48
4.2.7.2 A Test for the Difference
of Two Proportions 50
4.2.7.3 The Resampled Paired t Test 51
4.2.7.4 The A;-fold Cross-validated Paired
f Test 51
4.3 Computational Complexity 52
Contents xv
4.4 Comprehensibility 52
4.5 Scalability to Large Datasets 53
4.6 Robustness 55
4.7 Stability 55
4.8 Interestingness Measures 56
4.9 Overfitting and Underfitting 57
4.10 No Free Lunch Theorem 58
5. Splitting Criteria 61
5.1 Univariate Splitting Criteria 61
5.1.1 Overview 61
5.1.2 Impurity-based Criteria 61
5.1.3 Information Gain 62
5.1.4 Gini Index 62
5.1.5 Likelihood Ratio Chi-squared Statistics 63
5.1.6 DKM Criterion 63
5.1.7 Normalized Impurity-based Criteria 63
5.1.8 Gain Ratio 64
5.1.9 Distance Measure 64
5.1.10 Binary Criteria 64
5.1.11 Twoing Criterion 65
5.1.12 Orthogonal Criterion 65
5.1.13 Kolmogorov-Smirnov Criterion 66
5.1.14 AUC Splitting Criteria 66
5.1.15 Other Univariate Splitting Criteria 66
5.1.16 Comparison of Univariate Splitting Criteria . ... 66
5.2 Handling Missing Values 67
6. Pruning Trees 69
6.1 Stopping Criteria 69
6.2 Heuristic Pruning 69
6.2.1 Overview 69
6.2.2 Cost Complexity Pruning 70
6.2.3 Reduced Error Pruning 70
6.2.4 Minimum Error Pruning (MEP) 71
6.2.5 Pessimistic Pruning 71
6.2.6 Error-Based Pruning (EBP) 72
6.2.7 Minimum Description Length (MDL)
Pruning 73
6.2.8 Other Pruning Methods 73
xvi Data Mining with Decision Trees
6.2.9 Comparison of Pruning Methods 73
6.3 Optimal Pruning 74
7. Popular Decision Trees Induction Algorithms 77
7.1 Overview 77
7.2 ID3 77
7.3 C4.5 78
7.4 CART 79
7.5 CHAID 79
7.6 QUEST 80
7.7 Reference to Other Algorithms 80
7.8 Advantages and Disadvantages of Decision Trees 81
8. Beyond Classification Tasks 85
8.1 Introduction 85
8.2 Regression Trees 85
8.3 Survival Trees 86
8.4 Clustering Tree 89
8.4.1 Distance Measures 89
8.4.2 Minkowski: Distance Measures for Numeric
Attributes 90
8.4.2.1 Distance Measures for Binary
Attributes 90
8.4.2.2 Distance Measures for Nominal
Attributes 91
8.4.2.3 Distance Metrics for Ordinal
Attributes 91
8.4.2.4 Distance Metrics for Mixed-Type
Attributes 92
8.4.3 Similarity Functions 92
8.4.3.1 Cosine Measure 93
8.4.3.2 Pearson Correlation Measure 93
8.4.3.3 Extended Jaccard Measure 93
8.4.3.4 Dice Coefficient Measure 93
8.4.4 The OCCT Algorithm 93
8.5 Hidden Markov Model Trees 94
9. Decision Forests 99
9.1 Introduction 99
9.2 Back to the Roots 99
Contents xvii
9.3 Combination Methods 108
9.3.1 Weighting Methods 108
9.3.1.1 Majority Voting 108
9.3.1.2 Performance Weighting 109
9.3.1.3 Distribution Summation 109
9.3.1.4 Bayesian Combination 109
9.3.1.5 Dempster-Shafer 110
9.3.1.6 Vogging 110
9.3.1.7 Naïve Bayes 110
9.3.1.8 Entropy Weighting 110
9.3.1.9 Density-based Weighting Ill
9.3.1.10 DEA Weighting Method Ill
9.3.1.11 Logarithmic Opinion Pool Ill
9.3.1.12 Gating Network 112
9.3.1.13 Order Statistics 113
9.3.2 Meta-combination Methods 113
9.3.2.1 Stacking 113
9.3.2.2 Arbiter Trees 114
9.3.2.3 Combiner Trees 116
9.3.2.4 Grading 117
9.4 Classifier Dependency 118
9.4.1 Dependent Methods 118
9.4.1.1 Model-guided Instance Selection 118
9.4.1.2 Incremental Batch Learning 122
9.4.2 Independent Methods 122
9.4.2.1 Bagging 122
9.4.2.2 Wagging 124
9.4.2.3 Random Forest 125
9.4.2.4 Rotation Forest 126
9.4.2.5 Cross-validated Committees 129
9.5 Ensemble Diversity 130
9.5.1 Manipulating the Inducer 131
9.5.1.1 Manipulation of the Inducer s
Parameters 131
9.5.1.2 Starting Point in Hypothesis Space . . . 132
9.5.1.3 Hypothesis Space Traversal 132
9.5.1.3.1 Random-based Strategy . . . 132
9.5.1.3.2 Collect ive-Performance-based
Strategy 132
xviii
Data Mining with Decision Trees
9.5.2 Manipulating the Training Samples 133
9.5.2.1 Resampling 133
9.5.2.2 Creation 133
9.5.2.3 Partitioning 134
9.5.3 Manipulating the Target Attribute
Representation 134
9.5.4 Partitioning the Search Space 136
9.5.4.1 Divide and Conquer 136
9.5.4.2 Feature Subset-based Ensemble
Methods 137
9.5.4.2.1 Random-based Strategy . . . 138
9.5.4.2.2 Reduct-based Strategy .... 138
9.5.4.2.3 Collect ive-Perfor mance-
based Strategy 139
9.5.4.2.4 Feature Set Partitioning . . . 139
9.5.5 Multi-Inducers 142
9.5.6 Measuring the Diversity 143
9.6 Ensemble Size 144
9.6.1 Selecting the Ensemble Size 144
9.6.2 Pre-selection of the Ensemble Size 145
9.6.3 Selection of the Ensemble Size
while Training 145
9.6.4 Pruning — Post Selection
of the Ensemble Size 146
9.6.4.1 Pre-combining Pruning 146
9.6.4.2 Post-combining Pruning 146
9.7 Cross-Inducer 147
9.8 Multistrategy Ensemble Learning 148
9.9 Which Ensemble Method Should be Used? 148
9.10 Open Source for Decision Trees Forests 149
10. A Walk-through-guide for Using Decision Trees Software 151
10.1 Introduction 151
10.2 Weka 152
10.2.1 Training a Classification Tree 153
10.2.2 Building a Forest 158
10.3 R 159
10.3.1 Party Package 159
10.3.2 Forest 162
Contents xix
10.3.3 Other Types of Trees 163
10.3.4 The Rpart Package 164
10.3.5 RandomForest 165
11. Advanced Decision Trees 167
11.1 Oblivious Decision Trees 167
11.2 Online Adaptive Decision Trees 168
11.3 Lazy Tree 168
11.4 Option Tree 169
11.5 Lookahead 172
11.6 Oblique Decision Trees 172
11.7 Incremental Learning of Decision Trees 175
11.7.1 The Motives for Incremental Learning 175
11.7.2 The Inefficiency Challenge 176
11.7.3 The Concept Drift Challenge 177
11.8 Decision Trees Inducers for Large Datasets 179
11.8.1 Accelerating Tree Induction 180
11.8.2 Parallel Induction of Tree 182
12. Cost-sensitive Active and Proactive Learning
of Decision Trees 183
12.1 Overview 183
12.2 Type of Costs 184
12.3 Learning with Costs 185
12.4 Induction of Cost Sensitive Decision Trees 188
12.5 Active Learning 189
12.6 Proactive Data Mining 196
12.6.1 Changing the Input Data 197
12.6.2 Attribute Changing Cost
and Benefit Functions 198
12.6.3 Maximizing Utility 199
12.6.4 An Algorithmic Framework for Proactive
Data Mining 200
13. Feature Selection 203
13.1 Overview 203
13.2 The Curse of Dimensionality 203
13.3 Techniques for Feature Selection 206
13.3.1 Feature Filters 207
XX
Data Mining with Decision Vrees
13.3.1.1 FOCUS 207
13.3.1.2 LVF 207
13.3.1.3 Using a Learning Algorithm
as a Filter 207
13.3.1.4 An Information Theoretic
Feature Filter 208
13.3.1.5 RELIEF Algorithm 208
13.3.1.6 Simba and G-flip 208
13.3.1.7 Contextual Merit (CM) Algorithm . . . 209
13.3.2 Using Traditional Statistics for Filtering 209
13.3.2.1 Mallows Cp 209
13.3.2.2 AIC, BIC and F-ratio 209
13.3.2.3 Principal Component Analysis
(PCA) 210
13.3.2.4 Factor Analysis (FA) 210
13.3.2.5 Projection Pursuit (PP) 210
13.3.3 Wrappers 211
13.3.3.1 Wrappers for Decision Tree
Learners 211
13.4 Feature Selection as a means of Creating Ensembles . . . 211
13.5 Ensemble Methodology for Improving
Feature Selection 213
13.5.1 Independent Algorithmic Framework 215
13.5.2 Combining Procedure 216
13.5.2.1 Simple Weighted Voting 216
13.5.2.2 Using Artificial Contrasts 218
13.5.3 Feature Ensemble Generator 220
13.5.3.1 Multiple Feature Selectors 220
13.5.3.2 Bagging 221
13.6 Using Decision Trees for Feature Selection 221
13.7 Limitation of Feature Selection Methods 222
14. Fuzzy Decision Trees 225
14.1 Overview 225
14.2 Membership Function 226
14.3 Fuzzy Classification Problems 227
14.4 Fuzzy Set Operations 228
14.5 Fuzzy Classification Rules 229
14.6 Creating Fuzzy Decision Tree 230
Contents xxi
14.6.1 Fuzzifying Numeric Attributes 230
14.6.2 Inducing of Fuzzy Decision Tree 232
14.7 Simplifying the Decision Tree 234
14.8 Classification of New Instances 234
14.9 Other Fuzzy Decision Tree Inducers 234
15. Hybridization of Decision Trees with other Techniques 237
15.1 Introduction 237
15.2 A Framework for Instance-Space Decomposition 237
15.2.1 Stopping Rules 240
15.2.2 Splitting Rules 241
15.2.3 Split Validation Examinations 241
15.3 The Contrasted Population Miner
(CPOM) Algorithm 242
15.3.1 CPOM Outline 242
15.3.2 The Grouped Gain Ratio Splitting Rule 244
15.4 Induction of Decision Trees by an Evolutionary
Algorithm (EA) 246
16. Decision Trees and Recommender Systems 251
16.1 Introduction 251
16.2 Using Decision Trees for Recommending Items 252
16.2.1 RS-Adapted Decision Tree 253
16.2.2 Least Probable Intersections 257
16.3 Using Decision Trees for Preferences Elicitation 259
16.3.1 Static Methods 261
16.3.2 Dynamic Methods and Decision Trees 262
16.3.3 SVD-based CF Method 263
16.3.4 Pairwise Comparisons 264
16.3.5 Profile Representation 266
16.3.6 Selecting the Next Pairwise Comparison 267
16.3.7 Clustering the Items 269
16.3.8 Training a Lazy Decision Tree 270
Bibliography 273
Index 303
|
| any_adam_object | 1 |
| author | Roḳaḥ, Liʾor Maimon, Oded Z. |
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| discipline | Informatik |
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| id | DE-604.BV042191765 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T17:04:27Z |
| institution | BVB |
| isbn | 9789814590075 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027630832 |
| oclc_num | 897060282 |
| open_access_boolean | |
| owner | DE-29T DE-703 DE-11 |
| owner_facet | DE-29T DE-703 DE-11 |
| physical | xxi, 305 Seiten Illustrationen |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | World Scientific |
| record_format | marc |
| series | Series in machine perception and artificial intelligence |
| series2 | Series in machine perception and artificial intelligence |
| spellingShingle | Roḳaḥ, Liʾor Maimon, Oded Z. Data mining with decision trees theory and applications Series in machine perception and artificial intelligence Data Mining (DE-588)4428654-5 gnd Entscheidungsbaum (DE-588)4347788-4 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4347788-4 |
| title | Data mining with decision trees theory and applications |
| title_auth | Data mining with decision trees theory and applications |
| title_exact_search | Data mining with decision trees theory and applications |
| title_full | Data mining with decision trees theory and applications Lior Rokach, Oded Maimon |
| title_fullStr | Data mining with decision trees theory and applications Lior Rokach, Oded Maimon |
| title_full_unstemmed | Data mining with decision trees theory and applications Lior Rokach, Oded Maimon |
| title_short | Data mining with decision trees |
| title_sort | data mining with decision trees theory and applications |
| title_sub | theory and applications |
| topic | Data Mining (DE-588)4428654-5 gnd Entscheidungsbaum (DE-588)4347788-4 gnd |
| topic_facet | Data Mining Entscheidungsbaum |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027630832&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV006668231 |
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