anytime algorithms for learning anytime classifiers saher ... - Technion

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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 4 Anytime Learning of Cost-sensitive Trees 71 4.1 Biasing the Sample Towards Low-cost Trees . . . . . . . . . . . . 71 4.2 Evaluating Sample Trees . . . . . . . . . . . . . . . . . . . . . . 72 4.2.1 Estimating Test Costs . . . . . . . . . . . . . . . . . . . . 73 4.2.2 Estimating Misclassification Costs . . . . . . . . . . . . . 73 4.3 Choosing a Split . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3.1 Choosing a Split: Illustrative Examples . . . . . . . . . . 75 4.3.2 Choosing a Split when Attributes are Numeric . . . . . . 78 4.4 Cost-sensitive Pruning . . . . . . . . . . . . . . . . . . . . . . . . 78 4.5 Setting the Parameters of ACT . . . . . . . . . . . . . . . . . . . 79 4.6 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 80 4.6.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.6.3 Fixed-time Comparison . . . . . . . . . . . . . . . . . . . 85 4.6.4 Comparing the Accuracy of the Learned Models . . . . . 91 4.6.5 Comparison of Anytime Behavior . . . . . . . . . . . . . . 92 4.6.6 Random Costs . . . . . . . . . . . . . . . . . . . . . . . . 93 4.6.7 Nonuniform Misclassification Costs . . . . . . . . . . . . . 93 5 Anytime Learning of Anycost Classifiers 99 5.1 Pre-constract: Classification Budget is Known to the Learner . . 100 5.1.1 Top-down Induction of Anycost Trees . . . . . . . . . . . 101 5.1.2 Greedy TDIDT$ Instantiations . . . . . . . . . . . . . . . 102 5.1.3 The Pre-Contract-TATA Algorithm . . . . . . . . . . . . 102 5.1.4 Interruptible Learning of Pre-contract Classifiers . . . . . 104 5.2 Contract: When Allocation is Made Right Before Classification . 105 5.2.1 Repertoire of Trees . . . . . . . . . . . . . . . . . . . . . . 106 5.2.2 Learning a repertoire with Nonuniform Cost Gaps . . . . 107 5.3 Interruptible: No Pre-determined Classification Budget . . . . . . 108 5.4 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4.1 Pre-Contract Classification . . . . . . . . . . . . . . . . . 111 5.4.2 Contract Classification . . . . . . . . . . . . . . . . . . . . 115 5.4.3 Interruptible Classification . . . . . . . . . . . . . . . . . 118 6 Related Work 121 6.1 Non-greedy Cost-insensitive Decision Tree Inducers . . . . . . . . 121 6.1.1 Skewing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.1.2 Other Cost-insensitive Decision-Tree Inducers . . . . . . . 124 6.2 Anytime Induction of Other Cost-insensitive Classifiers . . . . . . 127 6.3 Cost-sensitive Classification . . . . . . . . . . . . . . . . . . . . . 128 6.4 Other Learning Components . . . . . . . . . . . . . . . . . . . . 132
Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 7 Conclusions 135 A Datasets 139 B Occam’s Razor Just Got Sharper 143 B.1 Occam’s Empirical Principle . . . . . . . . . . . . . . . . . . . . 144 B.2 Sampling the Version Space . . . . . . . . . . . . . . . . . . . . . 145 B.2.1 Defining the Version Space . . . . . . . . . . . . . . . . . 145 B.2.2 Sampling Techniques . . . . . . . . . . . . . . . . . . . . . 146 B.3 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 148 B.3.1 Consistent Decision Trees . . . . . . . . . . . . . . . . . . 149 B.3.2 Pruned Decision Trees . . . . . . . . . . . . . . . . . . . . 151 B.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 References 156 Hebrew Abstract i
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