A complete online-SVM pipeline for case-based reasoning system: a study on pipe defect detection system

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...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 24; no. 22; pp. 16917 - 16933
Main Authors Le, D. Van-Khoa, Chen, Zhiyuan, Wong, Yee Wan, Isa, Dino
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
Subjects
Artificial Intelligence
Computational Intelligence
Control
Decision trees
Defects
Design
Engineering
Feature selection
Injection molding
Insulin
Kernel functions
Machine learning
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Reasoning
Robotics
Subject specialists
Support vector machines
SVM
Online learning
Expert system
Case-based reasoning
Defect detection
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-04985-7