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DATA MINING WITH DECISION TREES (V69)

Theory and Applications

By: LIOR ROKACH & ODED MAIMON

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Hardcover | 29 December 2007

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264 Pages

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22.86 x 15.24 x 1.6

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This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer:

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Databases Data Mining Data Capture & Analysis Engineering & Technology Electronics & Communications Engineering Electronics Engineering

Preface	p. vii
Introduction to Decision Trees	p. 1
Data Mining and Knowledge Discovery	p. 1
Taxonomy of Data Mining Methods	p. 3
Supervised Methods	p. 4
Overview	p. 4
Classification Trees	p. 5
Characteristics of Classification Trees	p. 8
Tree Size	p. 9
The Hierarchical Nature of Decision Trees	p. 10
Relation to Rule Induction	p. 11
Growing Decision Trees	p. 13
Training Set	p. 13
Definition of the Classification Problem	p. 14
Induction Algorithms	p. 16
Probability Estimation in Decision Trees	p. 16
Laplace Correction	p. 17
No Match	p. 18
Algorithmic Framework for Decision Trees	p. 18
Stopping Criteria	p. 19
Evaluation of Classification Trees	p. 21
Overview	p. 21
Generalization Error	p. 21
Theoretical Estimation of Generalization Error	p. 22
Empirical Estimation of Generalization Error	p. 23
Alternatives to the Accuracy Measure	p. 24
The F-Measure	p. 25
Confusion Matrix	p. 27
Classifier Evaluation under Limited Resources	p. 28
ROC Curves	p. 30
Hit Rate Curve	p. 30
Qrecall (Quota Recall)	p. 32
Lift Curve	p. 32
Pearson Correlation Coefficient	p. 32
Area Under Curve (AUC)	p. 34
Average Hit Rate	p. 35
Average Qrecall	p. 35
Potential Extract Measure (PEM)	p. 36
Which Decision Tree Classifier is Better?	p. 40
McNemar's Test	p. 40
A Test for the Difference of Two Proportions	p. 41
The Resampled Paired t Test	p. 43
The k-fold Cross-validated Paired t Test	p. 43
Computational Complexity	p. 44
Comprehensibility	p. 44
Scalability to Large Datasets	p. 45
Robustness	p. 47
Stability	p. 47
Interestingness Measures	p. 48
Overfitting and Underfitting	p. 49
"No Free Lunch" Theorem	p. 50
Splitting Criteria	p. 53
Univariate Splitting Criteria	p. 53
Overview	p. 53
Impurity based Criteria	p. 53
Information Gain	p. 54
Gini Index	p. 55
Likelihood Ratio Chi-squared Statistics	p. 55
DKM Criterion	p. 55
Normalized Impurity-based Criteria	p. 56
Gain Ratio	p. 56
Distance Measure	p. 56
Binary Criteria	p. 57
Twoing Criterion	p. 57
Orthogonal Criterion	p. 58
Kolmogorov-Smirnov Criterion	p. 58
AUC Splitting Criteria	p. 58
Other Univariate Splitting Criteria	p. 59
Comparison of Univariate Splitting Criteria	p. 59
Handling Missing Values	p. 59
Pruning Trees	p. 63
Stopping Criteria	p. 63
Heuristic Pruning	p. 63
Overview	p. 63
Cost Complexity Pruning	p. 64
Reduced Error Pruning	p. 65
Minimum Error Pruning (MEP)	p. 65
Pessimistic Pruning	p. 65
Error-Based Pruning (EBP)	p. 66
Minimum Description Length (MDL) Pruning	p. 67
Other Pruning Methods	p. 67
Comparison of Pruning Methods	p. 68
Optimal Pruning	p. 68
Advanced Decision Trees	p. 71
Survey of Common Algorithms for Decision Tree Induction	p. 71
ID3	p. 71
C4.5	p. 71
CART	p. 71
CHAID	p. 72
QUEST	p. 73
Reference to Other Algorithms	p. 73
Advantages and Disadvantages of Decision Trees	p. 73
Oblivious Decision Trees	p. 76
Decision Trees Inducers for Large Datasets	p. 78
Online Adaptive Decision Trees	p. 79
Lazy Tree	p. 79
Option Tree	p. 80
Lookahead	p. 82
Oblique Decision Trees	p. 83
Decision Forests	p. 87
Overview	p. 87
Introduction	p. 87
Combination Methods	p. 90
Weighting Methods	p. 90
Majority Voting	p. 90
Performance Weighting	p. 91
Distribution Summation	p. 91
Bayesian Combination	p. 91
Dempster-Shafer	p. 92
Vogging	p. 92
Naive Bayes	p. 93
Entropy Weighting	p. 93
Density-based Weighting	p. 93
DEA Weighting Method	p. 93
Logarithmic Opinion Pool	p. 94
Gating Network	p. 94
Order Statistics	p. 95
Meta-combination Methods	p. 95
Stacking	p. 95
Arbiter Trees	p. 97
Combiner Trees	p. 99
Grading	p. 100
Classifier Dependency	p. 101
Dependent Methods	p. 101
Model-guided Instance Selection	p. 101
Incremental Batch Learning	p. 105
Independent Methods	p. 105
Bagging	p. 105
Wagging	p. 107
Random Forest	p. 108
Cross-validated Committees	p. 109
Ensemble Diversity	p. 109
Manipulating the Inducer	p. 110
Manipulation of the Inducer's Parameters	p. 111
Starting Point in Hypothesis Space	p. 111
Hypothesis Space Traversal	p. 111
Manipulating the Training Samples	p. 112
Resampling	p. 112
Creation	p. 113
Partitioning	p. 113
Manipulating the Target Attribute Representation	p. 114
Partitioning the Search Space	p. 115
Divide and Conquer	p. 116
Feature Subset-based Ensemble Methods	p. 117
Multi-Inducers	p. 121
Measuring the Diversity	p. 122
Ensemble Size	p. 124
Selecting the Ensemble Size	p. 124
Pre Selection of the Ensemble Size	p. 124
Selection of the Ensemble Size while Training	p. 125
Pruning - Post Selection of the Ensemble Size	p. 125
Pre-combining Pruning	p. 126
Post-combining Pruning	p. 126
Cross-Inducer	p. 127
Multistrategy Ensemble Learning	p. 127
Which Ensemble Method Should be Used?	p. 128
Open Source for Decision Trees Forests	p. 128
Incremental Learning of Decision Trees	p. 131
Overview	p. 131
The Motives for Incremental Learning	p. 131
The Inefficiency Challenge	p. 132
The Concept Drift Challenge	p. 133
Feature Selection	p. 137
Overview	p. 137
The "Curse of Dimensionality"	p. 137
Techniques for Feature Selection	p. 140
Feature Filters	p. 141
FOCUS	p. 141
LVF	p. 141
Using One Learning Algorithm as a Filter for Another	p. 141
An Information Theoretic Feature Filter	p. 142
An Instance Based Approach to Feature Selection - RELIEF	p. 142
Simba and G-flip	p. 142
Contextual Merit Algorithm	p. 143
Using Traditional Statistics for Filtering	p. 143
Mallows Cp	p. 143
AIC, BIC and F-ratio	p. 144
Principal Component Analysis (PCA)	p. 144
Factor Analysis (FA)	p. 145
Projection Pursuit	p. 145
Wrappers	p. 145
Wrappers for Decision Tree Learners	p. 145
Feature Selection as a Means of Creating Ensembles	p. 146
Ensemble Methodology as a Means for Improving Feature Selection	p. 147
Independent Algorithmic Framework	p. 149
Combining Procedure	p. 150
Simple Weighted Voting	p. 151
Naive Bayes Weighting using Artificial Contrasts	p. 152
Feature Ensemble Generator	p. 154
Multiple Feature Selectors	p. 154
Bagging	p. 156
Using Decision Trees for Feature Selection	p. 156
Limitation of Feature Selection Methods	p. 157
Fuzzy Decision Trees	p. 159
Overview	p. 159
Membership Function	p. 160
Fuzzy Classification Problems	p. 161
Fuzzy Set Operations	p. 163
Fuzzy Classification Rules	p. 164
Creating Fuzzy Decision Tree	p. 164
Fuzzifying Numeric Attributes	p. 165
Inducing of Fuzzy Decision Tree	p. 166
Simplifying the Decision Tree	p. 169
Classification of New Instances	p. 169
Other Fuzzy Decision Tree Inducers	p. 169
Hybridization of Decision Trees with other Techniques	p. 171
Introduction	p. 171
A Decision Tree Framework for Instance-Space Decomposition	p. 171
Stopping Rules	p. 174
Splitting Rules	p. 175
Split Validation Examinations	p. 175
The CPOM Algorithm	p. 176
CPOM Outline	p. 176
The Grouped Gain Ratio Splitting Rule	p. 177
Induction of Decision Trees by an Evolutionary Algorithm	p. 179
Sequence Classification Using Decision Trees	p. 187
Introduction	p. 187
Sequence Representation	p. 187
Pattern Discovery	p. 188
Pattern Selection	p. 190
Heuristics for Pattern Selection	p. 190
Correlation based Feature Selection	p. 191
Classifier Training	p. 191
Adjustment of Decision Trees	p. 192
Cascading Decision Trees	p. 192
Application of CREDT in Improving of Information Retrieval of Medical Narrative Reports	p. 193
Related Works	p. 195
Text Classification	p. 195
Part-of-speech Tagging	p. 198
Frameworks for Information Extraction	p. 198
Frameworks for Labeling Sequential Data	p. 199
Identifying Negative Context in Nondomain Specific Text (General NLP)	p. 199
Identifying Negative Context in Medical Narratives	p. 200
Works Based on Knowledge Engineering	p. 200
Works based on Machine Learning	p. 201
Using CREDT for Solving the Negation Problem	p. 201
The Process Overview	p. 201
Step 1: Corpus Preparation	p. 201
Step 1.1: Tagging	p. 202
Step 1.2: Sentence Boundaries	p. 202
Step 1.3: Manual Labeling	p. 203
Step 2: Patterns Creation	p. 203
Step 3: Patterns Selection	p. 206
Step 4: Classifier Training	p. 208
Cascade of Three Classifiers	p. 209
Bibliography	p. 215
Index	p. 243
Table of Contents provided by Ingram. All Rights Reserved.

DATA MINING WITH DECISION TREES (V69)

Theory and Applications

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

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