NDLI: Support vector machine in statistical process monitoring: a methodological and analytical review

Content Provider	Springer Nature Link
Author	Cuentas, Sandra Peñabaena Niebles, Rita Garcia, Ethel
Copyright Year	2016
Abstract	In statistical process monitoring, data mining algorithms are applied for control chart pattern recognition (CCPR) not only to detect but also to identify abnormal patterns associated with assignable causes. Recently, the use of support vector machines (SVMs) has achieved remarkable results in statistical process control applications due to its excellent generalization performance. Although there is a lot of research that highlights the superiority of support vector machine over other algorithms used in systems based on data mining control, there are no studies that analyze the design elements of a SVM classifier. Consequently, a comprehensive review, classification and an analysis of information found in the literature are presented in this paper regarding to the SVM classifier. The aim of this research is to provide researchers a starting point to potentiate the performance of the SVM classifier for assuring the best possible classification and improving the detection efficiency. Sixty-one research articles from 2001 to 2015 are critically analyzed based on their methodology following a classification scheme derived from the support vector classification framework. The analysis showed that the feature extraction and selection play a crucial role on the performance of classifiers. Studies revealed that using extracted features or a combination of raw data and feature extraction achieves better classification performances than using only raw data as an input. On the other hand, there is an ample gap to extend the research on internal structure configurations under various types of kernels, multi-classification, and ensemble approaches. Finally, this paper evidences that the application of nature inspired algorithms for kernel parameter selection in auto-correlated SVM-based process monitoring systems remains unexplored.
Starting Page	485
Ending Page	500
Page Count	16
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	91
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-11-23
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Quality control Support vector machines Pattern recognition Machine learning Industrial and Production Engineering Media Management Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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