An ensemble technique to handle missing data from sensors

Hussein Syed Mohammed, Nicholas Stepenosky, Robi Polikar

    Research output: Contribution to conferencePaperpeer-review

    6 Scopus citations

    Abstract

    Automated classification is often used in advanced systems to monitor system events. All data, and hence features from all sensors, must be present in order to make a meaningful classification. An ensemble approach, Learn ++.MF, was recently introduced that allows classification with up to 10% of feature missing, where several classifiers are trained on random subsets of the available sensor data. Given an instance with missing features, only those classifiers trained with the available features are then used in classification. In this paper, we present a modified approach that accommodates up to 30% missing features along with the effect of varying algorithm parameters.

    Original languageEnglish (US)
    Pages101-105
    Number of pages5
    StatePublished - Nov 21 2006
    Event2006 IEEE Sensors Applications Symposium - Houston, TX, United States
    Duration: Feb 7 2006Feb 9 2006

    Other

    Other2006 IEEE Sensors Applications Symposium
    CountryUnited States
    CityHouston, TX
    Period2/7/062/9/06

    All Science Journal Classification (ASJC) codes

    • Engineering(all)

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