INFUSE: Interactive feature selection for predictive modeling of high dimensional data

Josua Krause, Adam Perer, Enrico Bertini

    Research output: Contribution to journalArticle

    Abstract

    Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.

    Original languageEnglish (US)
    Article number6876047
    Pages (from-to)1614-1623
    Number of pages10
    JournalIEEE Transactions on Visualization and Computer Graphics
    Volume20
    Issue number12
    DOIs
    StatePublished - Dec 31 2014

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    Keywords

    • Predictive modeling
    • classification
    • feature selection
    • high-dimensional data
    • visual analytics

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Computer Graphics and Computer-Aided Design

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