Abstract
Recent developments in case-based reasoning system (CBR) have led to an interest in favoring machine learning (ML) approaches as a replacement for traditional weighted distance methods. However, valuable information obtained through a training process was relinquished as transferring to other phases. This paper proposed a complete pipeline integration of CBR using kernel method designated with support vector machine (SVM) as the main engine. Since the system requires learning SVM model to be invoked in every phase, the online learning mechanism is nominated to effectively update the model when a new case adjoins. The proposed full SVM-CBR integration has been successfully built into a pipe defect detection. The achieved result indicates a substantial improvement by transferring learning information accurately.
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Acknowledgements
Our thanks go to MOSTI grant from the University of Nottingham for supporting us in this Project, Project code 01-02-12-SF0360.
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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. Author D. Van-Khoa declares that he has no conflict of interest. Author Zhiyuan Chen declares that she has no conflict of interest. Author Wong Yee Wan declares that she has no conflict of interest. Author Dino Isa declares that he has no conflict of interest.
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Le, D.VK., Chen, Z., Wong, Y.W. et al. A complete online-SVM pipeline for case-based reasoning system: a study on pipe defect detection system. Soft Comput 24, 16917–16933 (2020). https://doi.org/10.1007/s00500-020-04985-7
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DOI: https://doi.org/10.1007/s00500-020-04985-7