What to measure next to improve decision making? On top-down task driven feature saliency

Lars Kai Hansen, Seliz Karadogan, Letizia Marchegiani

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    1 Downloads (Pure)

    Abstract

    Top-down attention is modeled as decision making based on incomplete information. We consider decisions made in a sequential measurement situation where initially only an incomplete input feature vector is available, however, where we are given the possibility to acquire additional input values among the missing features. The procecure thus poses the question what to do next? We take an information theoretical approach implemented for generality in a generative mixture model. The framework allows us reduce the decision about what to measure next in a classification problem to the estimation of a few onedimensional integrals per missing feature. We demonstrate the viability of the framework on four well-known classification problems.
    Original languageEnglish
    Title of host publication2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)
    PublisherIEEE
    Publication date2011
    ISBN (Print)978-1-4244-9890-1
    DOIs
    Publication statusPublished - 2011
    EventIEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain - Paris, France
    Duration: 1 Jan 2011 → …

    Conference

    ConferenceIEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain
    CityParis, France
    Period01/01/2011 → …

    Cite this

    Hansen, L. K., Karadogan, S., & Marchegiani, L. (2011). What to measure next to improve decision making? On top-down task driven feature saliency. In 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) IEEE. https://doi.org/10.1109/CCMB.2011.5952120