Control chart pattern recognition using an integrated model based on binary-tree support vector machine

Wu, Cang; Liu, Fei; Zhu, Bin

doi:10.1080/00207543.2014.948222

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…The second approach takes as input some shape features and also statistical features extracted from y (processed data). Based on the literature [7,8,9,10,11,12,13], the shape features considered in this study are:…”

Section: Shape and Statistical Features Selectionmentioning

confidence: 99%

“…This study considers offline classification and our analysis is based on Support Vector Machine (SVM) classifiers [37]. This classifier has been widely applied to process control [8,12,13,14,25]. Briefly, the SVM is a classifier that adjust an optimal hyperplane that provides the separation of the classes with the largest margin.…”

Section: Pattern Recognitionmentioning

confidence: 99%

“…Besides these features, a great number of methods are based on shape features [7,9,10,11], and its results have shown that the use of shape features as inputs of the classifier discriminates well the patterns, leading to higher classification rates. Ranaee, Ebrahimzadeh and Ghaderi [8], Zhang and Cheng [12] and Wu, Liu and Zhu15 [13] developed a method composed of statistical and shape features as inputs of a support vector machine (SVM) classifier. In [12], genetic algorithm was applied as an optimization tool to improve the performance of the multiclass SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], genetic algorithm was applied as an optimization tool to improve the performance of the multiclass SVM classifier. The model presented in [13] uses shape and statistical features as inputs of a binary-class SVM which identifies if the pattern is normal or not and, if the latter case is true, a binary-tree SVM is employed to recognize the pattern involved. Xanthopoulos and Razzaghi [14] also utilized the SVM classifier, but a weighted apporach.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Pelegrina

Duarte

Jutten

2016

Computers & Industrial Engineering

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International audienceControl charts are among the main tools in statistical process control (SPC) and have been extensively used for monitoring industrial processes. Currently, besides the single control charts, there is an interest in the concurrent ones. These graphics are characterized by the simultaneous presence of two or more single control charts. As a consequence, the individual patterns may be mixed, hindering the identification of a non-random pattern acting in the process; this phenomenon is refered as concurrent charts. In view of this problem, our first goal is to investigate the importance of an efficient separation step for pattern recognition. Then, we compare the efficiency of different Blind Source Separation (BSS) methods in the task of unmixing concurrent control charts. Furthermore, these BSS methods are combined with shape and statistical features in order to verify the performance of each one in pattern classification. In additional, the robustness of the better approach is tested in scenarios where there are different non-randomness levels and in cases with imbalanced dataset provided to the classifier. After simulating different patterns and applying several separation methods, the results have shown that the recognition rate is widely influenced by the separation and feature extraction steps and that the selection of efficient separation methods is fundamental to achieve high classification rates

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Section: Shape and Statistical Features Selectionmentioning

confidence: 99%

Section: Pattern Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Pelegrina

Duarte

Jutten

2016

Computers & Industrial Engineering

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“…The binary decision tree SVM of traffic information k classification hierarchical structure is shown in Figure 2. The BT-SVM solves the multi-class problems with a binary tree in which every node makes a binary decision using the binary-class SVM [21] .…”

Section: Bt-svmmentioning

confidence: 99%

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

Zhao

Sun

Zhao

et al. 2016

PROMET

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Support Vector Machines: A Review and Applications in Statistical Process Monitoring

Apsemidis¹,

Psarakis²

2020

Data Analysis and Applications 3

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Control chart pattern recognition using an integrated model based on binary-tree support vector machine

Cited by 26 publications

References 28 publications

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

Support Vector Machines: A Review and Applications in Statistical Process Monitoring

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