Application of machine learning in industrial radiographic testing

In this paper, we are empirically comparing the performance of neural nets and decision trees based on a data set for the detection of defects in welding seams. This data set was created by image feature extraction procedures working on digitized X-ray ®lms. We introduce a framework for distinguishing classi®cation methods. We found that more detailed analysis of the error rate is necessary in order to judge the performance of the learning and classi®cation method. However, the error rate cannot be the only criterion for comparing between the di€erent learning methods. This is a more complex selection process that involves more criteria that we are describing in this paper. Ó 2000 Elsevier Science B.V. All rights reserved.