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Synthetic Minority Oversampling and Linear Crossvalidated Support Vector Machine-based Recursive Feature Elimination to Classify Weld Flaws in Radiographic Images

In the field of nondestructive testing of welded components, the most important stages of automatic inspection systems concern the detection and classification of weld discontinuities. Limitations to correlating the heterogeneity and the discontinuity are imposed by the nature of the discontinuity: morphology, position, orientation, size, and so forth. Commonly seen weld discontinuities include cracks, linear inclusions, lack of penetration, and porosities. In their previous article, the authors were interested in discontinuity detection. In this paper, the authors attempted to provide a complete analysis for flaw classification taking into account the problem of data set unbalance using a synthetic minority oversampling technique. In addition, a cross-validated linear support vector machine-based recursive feature elimination algorithm was developed to perform feature selection, allowing better generalization performance.

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