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

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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 Average % Standard Cost Average % Standard Cost 50 45 40 35 30 25 20 15 C4.5 EG2 DTMC ICET ACT 10 γ/8γ γ/4γ γ/2γ γ/γ 2γ/γ 4γ/γ 8γ/γ 60 55 50 45 40 35 30 25 20 15 Misclassification Cost FP/FN C4.5 EG2 DTMC ICET ACT 10 γ/8γ γ/4γ γ/2γ γ/γ 2γ/γ 4γ/γ 8γ/γ Misclassification Cost FP/FN Figure 4.12: Comparison of C4.5, EG2, DTMC, ACT, and ICET when misclassification costs are nonuniform. The misclassification costs are represented as a pair (FP/FN). FP denotes the penalty for a false positive and FM the penalty for a false negative. γ denotes the basic mc unit. The figures plot the average cost as a function of the ratio between FP and FN costs, for γ = 500 (top) and γ = 5000 (bottom). 98
Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 Chapter 5 Anytime Learning of Anycost Classifiers Assume that a hardware manufacturer who uses a machine-learning based tool for assuring the quality of produced chips. In realtime, each chip in the pipeline is scanned and several features can be extracted from the image. The features vary in their computation time. The manufacturer trains the component using thousands of chips whose validity is known. Because the training is done offline, the manufacturer can provide the values of all possible features, regardless of their computation time. In realtime, however, the model must make a decision within 2 seconds. Therefore, for each chip, the classifier may use features whose total computation time is at most 2 seconds. Alternatively, the manufacturer might want to provide the classifier with a different maximal time, depending on the case, or even configuring the classifier to utilize time until the next item arrives and then querying it for a decision. To act under these different resource-bounded classification scenarios, our framework should produce predictive models that can control testing costs efficiently, and should also be able to exploit additional learning resources in order to improve the produced models. For the first requirement, i.e., resource-bounded classification, a decision-tree based classifier would make an ideal candidate. When classifying a new case, decision trees ask only for the values of the tests on a single path from the root to one of the leaves. Tests that do not appear on the actual path need not be administered. Decision tree models are also considered attractive due to their interpretability (Hastie et al., 2001), an important criterion for evaluating a classifier (Craven, 1996), their simplicity of use, and their accuracy, which has been shown to be competitive with other classifiers for several learning tasks. Decision trees, however, cannot be used as is: when the classification budget does not allow exploring the entire path, the tree cannot make a decision. This 99
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anytime algorithms for learning anytime classifiers saher ... - Technion

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