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2012 (Vol. 4, Issue: 24)
Article Information:

Application of Multidimensional Chain classifiers to Eddy Current Images for Defect Characterization

S. Shuaib Ahmed, B.P.C. Rao and T. Jayakumar
Corresponding Author:  B.P.C. Rao 

Key words:  Chain classifier, eddy current testing, multidimensional learning, nondestructive evaluation, radial basis function , support vector machines,
Vol. 4 , (24): 5544-5547
Submitted Accepted Published
April 07, 2012 April 25, 2012 December 15, 2012

Multidimensional learning problem deals with learning a function that maps a vector of input features to a vector of class labels. Dependency between the classes is not taken into account while constructing independent classifiers for each component class of vector. To counteract this limitation, Chain Classifiers (CC) approach for multidimensional learning is proposed in this study. In this approach, the information of class dependency is passed along a chain. Radial Basis Functions (RBF) and Support Vector Machines (SVM) are used as core for CC. Studies on multidimensional dataset of images obtained from simulated eddy current non-destructive evaluation of a stainless steel plate with sub-surface defects clearly indicate that the performance of the chain classifier is superior to the independent classifiers.
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  Cite this Reference:
S. Shuaib Ahmed, B.P.C. Rao and T. Jayakumar, 2012. Application of Multidimensional Chain classifiers to Eddy Current Images for Defect Characterization.  Research Journal of Applied Sciences, Engineering and Technology, 4(24): 5544-5547.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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