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Automation Technology, Control Technology     
Fault classification based on modified active learning and weighted SVM
ZHU Dong-yang, SHEN Jing-yi, HUANG Wei-ping, LIANG Jun
College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China
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A new method based on modified Best versus Second-Best (BvSB) active learning and weighted support vector machine for fault classification in real-world industrial process was presented in order to solve the problems that large-scale labeled fault samples are not easy to acquire, labeling cost is expensive, datasets are usually imbalanced and contaminated with outliers. An improved BvSB selection method was proposed to iteratively select the most valuable data and query their labels by comprehensively measuring the informativeness and representativeness of unlabeled instances and reducing the impact of outliers. WeightedSVM was introduced to tackle the impact of imbalanced class distribution on active learning and classification accuracy, using different weight factors for classes and individual samples. A new efficient method of determining the penalty coefficient was presented. Case study on TE process verifies that the proposed approach can achieve superior classification accuracy while reducing the labeling cost.

Published: 25 April 2017
CLC:  TP 181  
Cite this article:

ZHU Dong-yang, SHEN Jing-yi, HUANG Wei-ping, LIANG Jun. Fault classification based on modified active learning and weighted SVM. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 697-705.



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