Abstract
Incremental One-Class Support Vector Machine (OCSVM) methods provide critical advantages in practical applications, as they are able to capture variations of the positive samples over time. This paper proposes a novel self-advised incremental OCSVM algorithm, which decides whether an incremental step is required to update its model or not. As opposed to existing method, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM as proxies for data in order to determine which new data points should be included in the training set and trigger an update of the model’s coefficients. This new online OCSVM algorithm was extensively evaluated using real data from Structural Health Monitoring (SHM) case studies. These results showed that this new online method provided significant improvements in classification error rates, was able to assimilate the changes in the positive data distribution over the time, and maintained a high damage detection accuracy in these SHM cases.
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Notes
- 1.
The data for February 2016 were discarded due to a known instrumentation problem, which appeared and was fixed during that period.
References
Anaissi, A., Goyal, M.: SVM-based association rules for knowledge discovery and classification. In: 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pp. 1–5. IEEE (2015)
Anaissi, A., Goyal, M., Catchpoole, D.R., Braytee, A., Kennedy, P.J.: Ensemble feature learning of genomic data using support vector machine. PloS One 11(6), e0157330 (2016)
Anaissi, A., Khoa, N.L.D., Mustapha, S., Alamdari, M.M., Braytee, A., Wang, Y., Chen, F.: Adaptive one-class support vector machine for damage detection in structural health monitoring. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS, vol. 10234, pp. 42–57. Springer, Cham (2017). doi:10.1007/978-3-319-57454-7_4
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: NIPS, vol. 13 (2000)
Comanducci, G., Magalhães, F., Ubertini, F., Cunha, Á.: On vibration-based damage detection by multivariate statistical techniques: application to a long-span arch bridge. Struct. Health Monit. 15(5), 505–524 (2016)
Davy, M., Desobry, F., Gretton, A., Doncarli, C.: An online support vector machine for abnormal events detection. Sig. Process. 86(8), 2009–2025 (2006)
Diehl, C.P., Cauwenberghs, G.: SVM incremental learning, adaptation and optimization. In: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 4, pp. 2685–2690. IEEE (2003)
Khoa, N.L., Zhang, B., Wang, Y., Chen, F., Mustapha, S.: Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit. 13(4), 406–417 (2014)
Khoa, N.L.D., Zhang, B., Wang, Y., Liu, W., Chen, F., Mustapha, S., Runcie, P.: On damage identification in civil structures using tensor analysis. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9077, pp. 459–471. Springer, Cham (2015). doi:10.1007/978-3-319-18038-0_36
Laskov, P., Gehl, C., Krüger, S., Müller, K.R.: Incremental support vector learning: analysis, implementation and applications. J. Mach. Learn. Res. 7, 1909–1936 (2006)
Maali, Y., Al-Jumaily, A.: Self-advising support vector machine. Knowl. Based Syst. 52, 214–222 (2013)
Magalhães, F., Cunha, Á., Caetano, E.: Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mech. Syst. Sig. Process. 28, 212–228 (2012)
Runcie, P., Mustapha, S., Rakotoarivelo, T.: Advances in structural health monitoring system architecture. In: Proceedings of the Fourth International Symposium on Life-Cycle Civil Engineering, IALCCE, vol. 14 (2014)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Schölkopf, B., Smola, A.J.: Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT press, Cambridge (2002)
Wang, T., Chen, J., Zhou, Y., Snoussi, H.: Online least squares one-class support vector machines-based abnormal visual event detection. Sensors 13(12), 17130–17155 (2013)
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Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T., Alamdari, M.M., Wang, Y. (2017). Self-advised Incremental One-Class Support Vector Machines: An Application in Structural Health Monitoring. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_51
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