Data representation and feature selection for colorimetric sensor arrays used as explosives detectors

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    Abstract

    Within the framework of the strategic research project Xsense at the Technical University of Denmark, we are developing a colorimetric sensor array which can be useful for detection of explosives like DNT, TNT, HMX, RDX and TATP and identification of volatile organic compounds in the presence of water vapor in air. In order to analyze colorimetric sensors with statistical methods, the sensory output must be put into numerical form suitable for analysis. We present new ways of extracting features from a colorimetric sensor and determine the quality and robustness of these features using machine learning classifiers. Sensors, and in particular explosive sensors, must not only be able to classify explosives, they must also be able to measure the certainty of the classifier regarding the decision it has made. This means there is a need for classifiers that not only give a decision, but also give a posterior probability about the decision. We will compare K-nearest neighbor, artificial neural networks and sparse logistic regression for colorimetric sensor data analysis. Using the sparse solutions we perform feature selection and feature ranking and compare to Gram-Schmidt orthogonalization.
    Original languageEnglish
    Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
    PublisherIEEE
    Publication date2011
    ISBN (Print)978-1-4577-1621-8
    ISBN (Electronic)978-1-4577-1622-5
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Workshop on Machine Learning for Signal Processing - Beijing, China
    Duration: 1 Jan 2011 → …
    http://mlsp2011.conwiz.dk/

    Conference

    Conference2011 IEEE International Workshop on Machine Learning for Signal Processing
    CountryChina
    CityBeijing
    Period01/01/2011 → …
    Internet address
    SeriesMachine Learning for Signal Processing
    ISSN1551-2541

    Cite this

    Alstrøm, T. S., Larsen, J., Kostesha, N., Jakobsen, M. H., & Boisen, A. (2011). Data representation and feature selection for colorimetric sensor arrays used as explosives detectors. In 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) IEEE. Machine Learning for Signal Processing https://doi.org/10.1109/MLSP.2011.6064615