The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
MetaCost: a general method for making classifiers cost-sensitive
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Learning cost-sensitive active classifiers
Artificial Intelligence
Pruning Improves Heuristic Search for Cost-Sensitive Learning
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Inducing Cost-Sensitive Trees via Instance Weighting
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Methods for cost-sensitive learning
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"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Test Strategies for Cost-Sensitive Decision Trees
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Fast data acquisition in cost-sensitive learning
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We propose a simple, novel and yet effective method for building and testing decision trees that minimizes the sum of the misclassification and test costs. More specifically, we first put forward an original and simple splitting criterion for attribute selection in tree building. Our tree-building algorithm has many desirable properties for a cost-sensitive learning system that must account for both types of costs. Then, assuming that the test cases may have a large number of missing values, we design several intelligent test strategies that can suggest ways of obtaining the missing values at a cost in order to minimize the total cost. We experimentally compare these strategies and C4.5, and demonstrate that our new algorithms significantly outperform C4.5 and its variations. In addition, our algorithm's complexity is similar to that of C4.5, and is much lower than that of previous work. Our work is useful for many diagnostic tasks which must factor in the misclassification and test costs for obtaining missing information.