Feature value acquisition in testing: A sequential batch test algorithm

Victor S. Sheng, Charles X. Ling

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

16 Scopus citations

Abstract

In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make an accurate diagnosis of patient diseases. While doing so they have to make a trade-off between the cost of the tests and possible misdiagnosis. In this paper, we use cost-sensitive learning to model this process. We assume that test examples (new patients) may contain missing values, and their actual values can be acquired at cost (similar to doing medical tests) in order to reduce misclassification errors (misdiagnosis). We propose a novel Sequential Batch Test algorithm that can acquire sets of attribute values in sequence, similar to sets of medical tests ordered by doctors in sequence. The goal of our algorithm is to minimize the total cost (i.e., the trade-off) of acquiring attribute values and misclassifications. We demonstrate the effectiveness of our algorithm, and show that it outperforms previous methods significantly. Our algorithm can be readily applied in real-world diagnosis tasks. A case study on the heart disease is given in the paper.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Pages809-816
Number of pages8
DOIs
StatePublished - 2006
Event23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006

Publication series

NameACM International Conference Proceeding Series
Volume148

Conference

Conference23rd International Conference on Machine Learning, ICML 2006
CountryUnited States
CityPittsburgh, PA
Period06/25/0606/29/06

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