Title :
A support vector machine-based pattern recognizer using selected features for control chart patterns analysis
Author :
Cheng, C.S. ; Cheng, H.P. ; Huang, K.K.
Author_Institution :
Dept. of Ind. Eng. & Manage., Yuan-Ze Univ., Nei-Li, Taiwan
Abstract :
In this paper we review two implementation modes of control chart pattern recognition and introduce a new research problem concerning pattern displacement problem in the ¿recognition only when necessary¿ mode. A set of features are developed by taking the pattern displacement into account. Simulation studies indicate that an SVM-based pattern recognizer with features as input vector performs significantly better than that of using raw data as inputs.
Keywords :
control charts; pattern recognition; statistical process control; support vector machines; control chart patterns analysis; pattern recognizer; statistical process control; support vector machine; Artificial neural networks; Control charts; Data mining; Feature extraction; Monitoring; Pattern analysis; Pattern recognition; Stability; Support vector machines; Testing; SVM; features; pattern recognition;
Conference_Titel :
Industrial Engineering and Engineering Management, 2009. IEEM 2009. IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4869-2
Electronic_ISBN :
978-1-4244-4870-8
DOI :
10.1109/IEEM.2009.5373318
