The stochastic score classification problem

Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Consider the following Stochastic Score Classification Problem. A doctor is assessing a patient's risk of developing a certain disease, and can perform n tests on the patient. Each test has a binary outcome, positive or negative. A positive result is an indication of risk, and a patient's score is the total number of positive test results. Test results are accurate. The doctor needs to classify the patient into one of B risk classes, depending on the score (e.g., LOW, MEDIUM, and HIGH risk). Each of these classes corresponds to a contiguous range of scores. Test i has probability pi of being positive, and it costs ci to perform. To reduce costs, instead of performing all tests, the doctor will perform them sequentially and stop testing when it is possible to determine the patient's risk category. The problem is to determine the order in which the doctor should perform the tests, so as to minimize expected testing cost. We provide approximation algorithms for adaptive and non-adaptive versions of this problem, and pose a number of open questions.

    Original languageEnglish (US)
    Title of host publication26th European Symposium on Algorithms, ESA 2018
    EditorsHannah Bast, Grzegorz Herman, Yossi Azar
    PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
    ISBN (Print)9783959770811
    DOIs
    StatePublished - Aug 1 2018
    Event26th European Symposium on Algorithms, ESA 2018 - Helsinki, Finland
    Duration: Aug 20 2018Aug 22 2018

    Publication series

    NameLeibniz International Proceedings in Informatics, LIPIcs
    Volume112
    ISSN (Print)1868-8969

    Other

    Other26th European Symposium on Algorithms, ESA 2018
    CountryFinland
    CityHelsinki
    Period8/20/188/22/18

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    Keywords

    • Adaptivity
    • Approximation algorithms
    • Sequential testing
    • Stochastic probing
    • Symmetric boolean functions

    ASJC Scopus subject areas

    • Software

    Cite this

    Gkenosis, D., Grammel, N., Hellerstein, L., & Kletenik, D. (2018). The stochastic score classification problem. In H. Bast, G. Herman, & Y. Azar (Eds.), 26th European Symposium on Algorithms, ESA 2018 (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 112). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ESA.2018.36