Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bond rating using support vector machine
Intelligent Data Analysis
Feature-based recognition of control chart patterns
Computers and Industrial Engineering
A hybrid system for SPC concurrent pattern recognition
Advanced Engineering Informatics
Independent component analysis-based defect detection in patterned liquid crystal display surfaces
Image and Vision Computing
Hybrid Abnormal Patterns Recognition of Control Chart Using Support Vector Machining
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Recognition of control chart patterns using improved selection of features
Computers and Industrial Engineering
A control chart pattern recognition system using a statistical correlation coefficient method
Computers and Industrial Engineering
Features extraction and analysis for classifying causable patterns in control charts
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
An overview of statistical learning theory
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Integrated use of ICA and ANN to recognize the mixture control chart patterns in a process
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Journal of Intelligent Manufacturing
Hybrid intelligent modeling schemes for heart disease classification
Applied Soft Computing
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Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.