This paper describes a hybrid feature selection algorithm that uses three different statistical measurements to evaluate features: between-class pairwise distance, linear separability, and overlapped feature histogram. The paper presents detailed steps of each feature measurement. The hybrid feature selection algorithm applies the Bayesian EM (expectation maximization) to the features ranked by the three measurements referred to above to select a sub-optimal feature set. The hybrid feature selection algorithm can be used as a preprocessing in a classification system and is independent of the classifier to be used in the subsequence stage. We applied the hybrid feature selection algorithm to select vehicle signal features for fault diagnosis. Our experiments show that the hybrid algorithm provides a sub-optimal feature set that can be used to train a classifier to have very good generalization capability.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatic feature selection - a hybrid statistical approach


    Contributors:
    Yi Lu Murphey (author) / Hong Guo (author)


    Publication date :

    2000


    Size :

    4 Seiten, 8 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    AUTOMATIC VEHICLE FEATURE SELECTION

    PRZYBYLSKI MATTHEW G / ZINSER CRAIG H / CREEHAN JAMES SCOTT | European Patent Office | 2024

    Free access

    Automatic vehicle feature selection

    PUZBILSKI MICHAEL G / SINSER CHRISTOPHER H / CREAHAN JAMES S | European Patent Office | 2024

    Free access

    Feature Detection with Automatic Scale Selection

    Lindeberg, T. | British Library Online Contents | 1998


    Hybrid IG and GA based Feature Selection Approach for Text Categorization

    Thejaswee, Manda / Srilakshmi, P. / Karuna, G. et al. | IEEE | 2020


    Feature Tracking with Automatic Selection of Spatial Scales

    Bretzner, L. / Lindeberg, T. | British Library Online Contents | 1998