Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring
Anaissi, Ali; Nguyen, Khoa ; Rakotoarivelo, Thierry; Makki Alamdari, Mehri; Wang, Yang
2018-11-14
Journal Article
Transactions on Intelligent Systems and Technology (TIST)
9
6
1-20
One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e. healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This paper proposes a novel adaptive self-advised online OCSVM, which incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the 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 all case studies.
ACM
Pattern Recognition and Data Mining
https://doi.org/10.1145/3230708
EP182802
Journal article - Refereed
English
Anaissi, Ali; Nguyen, Khoa; Rakotoarivelo, Thierry; Makki Alamdari, Mehri; Wang, Yang. Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring. Transactions on Intelligent Systems and Technology (TIST). 2018; 9(6):1-20. https://doi.org/10.1145/3230708
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