A comparison between neural networks and decision trees based on data from 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 films. We introduce a framework for distinguishing classification methods. We found that more detailed analysis of the error rate is necessary in order to judge the performance of the learning and classification method. However, the error rate cannot be the only criterion for comparing between the different learning methods. This is a more complex selection process that involves more criteria that we are describing in this paper.