IDEAS home Printed from https://ideas.repec.org/a/inm/orijds/v3y2024i2p179-202.html
   My bibliography  Save this article

Conjecturing-Based Discovery of Patterns in Data

Author

Listed:
  • J. Paul Brooks

    (Department of Information Systems, Virginia Commonwealth University, Richmond, Virginia 23284)

  • David J. Edwards

    (Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284)

  • Craig E. Larson

    (Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia 23284)

  • Nico Van Cleemput

    (Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium)

Abstract

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed C onjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making.

Suggested Citation

  • J. Paul Brooks & David J. Edwards & Craig E. Larson & Nico Van Cleemput, 2024. "Conjecturing-Based Discovery of Patterns in Data," INFORMS Joural on Data Science, INFORMS, vol. 3(2), pages 179-202, October.
    • Handle: RePEc:inm:orijds:v:3:y:2024:i:2:p:179-202
    DOI: 10.1287/ijds.2021.0043
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijds.2021.0043
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijds.2021.0043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pat Langley, 2019. "Scientific discovery, causal explanation, and process model induction," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 18(1), pages 43-56, June.
    2. Lorillee Tallorin & JiaLei Wang & Woojoo E. Kim & Swagat Sahu & Nicolas M. Kosa & Pu Yang & Matthew Thompson & Michael K. Gilson & Peter I. Frazier & Michael D. Burkart & Nathan C. Gianneschi, 2018. "Discovering de novo peptide substrates for enzymes using machine learning," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijds:v:3:y:2024:i:2:p:179-202. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.