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  • Classification ensembles for shaft test data : Empirical evaluation

    Author(s)
    Lee, K
    Estivill-Castro, V
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2005
    Metadata
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    Abstract
    A-scans from ultrasonic testing of long shafts are complex signals. The discrimination of different types of echoes is of importance for non-destructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like Artificial Neural Networks and Support Vector Machines. This paper confirms the observation that there seems to be uncorrelated errors among the variants explored in the past, and therefore an ensemble of classifiers is to achieve better discrimination accuracy. We explore the diverse possibilities of heterogeneous and homogeneous ensembles, ...
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    A-scans from ultrasonic testing of long shafts are complex signals. The discrimination of different types of echoes is of importance for non-destructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like Artificial Neural Networks and Support Vector Machines. This paper confirms the observation that there seems to be uncorrelated errors among the variants explored in the past, and therefore an ensemble of classifiers is to achieve better discrimination accuracy. We explore the diverse possibilities of heterogeneous and homogeneous ensembles, combination techniques, feature extraction methods and classifiers types and determine guidelines for heterogeneous combinations that result in superior performance.
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    Journal Title
    International Journal of Simulation: Systems, Science and Technology
    Volume
    6
    Issue
    10-11
    DOI
    https://doi.org/10.1109/ICHIS.2004.31
    Copyright Statement
    © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Publication URI
    http://hdl.handle.net/10072/408074
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    • Journal articles

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