Simple test strategies for cost-sensitive decision trees

Shengli Sheng, Charles X. Ling, Qiang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages365-376
Number of pages12
DOIs
StatePublished - 2005
Event16th European Conference on Machine Learning, ECML 2005 - Porto, Portugal
Duration: Oct 3 2005Oct 7 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3720 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Machine Learning, ECML 2005
CountryPortugal
CityPorto
Period10/3/0510/7/05

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