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Multi classification support vector machine

2015-06-19 15:06:31
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Application background

% INPUT:                                                                                                                                      
% data: time series data with label and weights, If we run SVM, the weights for all data samples are one                          
% m: the size of Normal class
% n: the size of abnormal class                          
% w: window length                                   
% t: parameter of abnormal pattern                             
% mod: if mod==1 SVM is running elseif mod==2 WSVM is running                                      
% OUTPUT:                                                                                          
% meanROC: the average of ROC over 10-fold cross-validation
% meanACC: the average of Accuracy over 10-fold cross-validation

Key Technology

Manual inspection and evaluation of quality control data is a tedious task that requires the undistracted
attention of specialized personnel. On the other hand, automated monitoring of a production process is
necessary, not only for real time product quality assessment, but also for potential machinery malfunction
diagnosis. For this reason, control chart pattern recognition (CCPR) methods have received a lot of
attention over the last two decades. Current state-of-the-art control monitoring methodology includes
K charts which are based on support vector machines (SVM). Although K charts have some profound benefits,
their performance deteriorate when the learning examples for the normal class greatly outnumbers
the ones for the abnormal class. Such problems are termed imbalanced and represent the vast majority of
the real life control pattern classification problems. Original SVM demonstrate poor performance when
applied directly to these problems. In this paper, we propose the use of weighted support vector
machines (WSVM) for automated process monitoring and early fault diagnosis. We show the benefits
of WSVM over traditional SVM, compare them under various fault scenarios. We evaluate the proposed
algorithm in binary and multi-class environments for the most popular abnormal quality control patterns
as well as a real application from wafer manufacturing industry.
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File list

Tips: You can preview the content of files by clicking file names^_^
Name Size Date
GenerateData.m2.55 kB19-03-14 18:47
Main.m2.33 kB19-03-14 18:47
wsvmmodel.m4.29 kB19-03-14 18:47
COPYRIGHT_SVMLib1.46 kB19-03-14 18:47
FaraDars.org.url109.00 B10-05-15 14:45
license.txt1.29 kB19-03-14 18:47
<MATLAB>0.00 B91% 10-05-15
MatlabSite.com.url111.00 B10-05-15 14:45
GenDataMulti.m2.95 kB19-03-14 18:47
Main.m2.01 kB19-03-14 18:47
multiclass.mat222.00 B19-03-14 18:47
wsvmmodelmulti.m11.92 kB19-03-14 18:47
Neural-Networks-Refs.pdf745.76 kB10-05-15 13:01
WSVMToolbox.png47.40 kB19-03-14 18:47
WSVMToolbox_Guide.pdf145.27 kB19-03-14 18:47
<._Binary>0.00 B77% 19-03-14
._COPYRIGHT_SVMLib413.00 B19-03-14 18:47
<._Multiclass>0.00 B77% 19-03-14
._WSVMToolbox.png222.00 B19-03-14 18:47
._WSVMToolbox_Guide.pdf222.00 B19-03-14 18:47
._GenerateData.m222.00 B19-03-14 18:47
._Main.m222.00 B19-03-14 18:47
._wsvmmodel.m222.00 B19-03-14 18:47
._GenDataMulti.m222.00 B19-03-14 18:47
._Main.m222.00 B19-03-14 18:47
._multiclass.mat222.00 B19-03-14 18:47
._wsvmmodelmulti.m222.00 B19-03-14 18:47
<Binary>0.00 B0% 10-05-15
<Multiclass>0.00 B0% 10-05-15
<Binary>0.00 B0% 10-05-15
<Multiclass>0.00 B0% 10-05-15
<__MACOSX>0.00 B10-05-15 15:22
<matlab-support-vector-0002_www.matlabiste.com>0.00 B27-05-15 21:07
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Multi classification support vector machine (1.58 MB)(60.01 kB)

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