A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA

DOI

10.2991/ameii-15.2015.262How to use a DOI?

Keywords

defects classification; PCA; LDA.

Abstract

To improve the classification accuracy of defects, a novel algorithm has been developed based on dual-parameters optimization of the principal component analysis (PCA) and the linear discriminant analysis (LDA). The original defect images are transformed to eigen-defects by PCA which contains all features of these defects. Then, LDA is used to classify eigen-defects. The optimal parameters of PCA and LDA are given when the PCA-LDA model gets the maximum value of classification accuracy. For estimating the actual classification accuracy of the proposed method in a concrete system, Bootstrap evaluation method is introduced. The experimental result demonstrates that the accuracy of this method is 91.12%, which promotes the accuracy by 0.37%, 3.61% and 8.72% comparing with PCA-SVM, SVM and MLP-ANN.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weilei Mu
AU  - Guijie Liu
AU  - Peng Liu
AU  - Xiaojie Tian
PY  - 2015/04
DA  - 2015/04
TI  - A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1425
EP  - 1429
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.262
DO  - 10.2991/ameii-15.2015.262
ID  - Mu2015/04
ER  -