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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Simple Test Strategies for Cost-Sensitive Decision Trees
verfasst von : Shengli Sheng, Charles X. Ling, Qiang Yang
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that minimizes the total cost of tests and misclassifications. Then assuming test examples may contain unknown attributes whose values can be obtained at a cost (the test cost), we design several novel test strategies which attempt to minimize the total cost of tests and misclassifications for each test example. We empirically evaluate our tree-building and various test strategies, and show that they are very effective. Our results can be readily applied to real-world diagnosis tasks, such as medical diagnosis where doctors must try to determine what tests (e.g., blood tests) should be ordered for a patient to minimize the total cost of tests and misclassifications (misdiagnosis). A case study on heart disease is given throughout the paper.