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Automated parameter tuning in one-class support vector machine: an application for damage detection

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Abstract

Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one-class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter \(\sigma \). This paper proposes a new algorithm named appropriate distance to the enclosing surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of \(\sigma \) which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge and vehicle-mounted sensors for damage detection, in addition to five public data sets. The results obtained by ADES are compared to those of variance–mean, maximum distance and MIES methods. The results of the ADES approach outperform these state-of-the-art methods, especially on the bridge and road data sets. Experiments on these data sets show that an average 3% better accuracy is achieved by ADES over these state-of-the-art methods. The designed ADES algorithm is an appropriate choice to identify the optimal value of \(\sigma \) for OCSVM, especially in high-dimensional data sets.

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  1. http://archive.ics.uci.edu/ml/datasets.html.

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Acknowledgements

The authors would like to thank the Road and Maritime Services (RMS) in New South Wales and Data61, CSIRO, for provision of the support and testing facilities for this research work.

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Authors and Affiliations

  1. Faculty of Engineering and IT, The University of Sydney, Sydney, NSW, 2006, Australia

    Ali Anaissi

  2. Data61|CSIRO, 13 Garden Street, Eveleigh, NSW, 2015, Australia

    Nguyen Lu Dang Khoa & Yang Wang

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  1. Ali Anaissi
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  2. Nguyen Lu Dang Khoa
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  3. Yang Wang
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Correspondence to Ali Anaissi.

Additional information

This paper is an extension version of the PAKDD’2017 Long Presentation paper “Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring” [4].

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Anaissi, A., Khoa, N.L.D. & Wang, Y. Automated parameter tuning in one-class support vector machine: an application for damage detection. Int J Data Sci Anal 6, 311–325 (2018). https://doi.org/10.1007/s41060-018-0151-9

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