Improving the accuracy of computer-aided radiographic weld inspection by feature selection

This paper presents new results of our continuous effort to develop a computer-aided radiographic weld inspection system. The focus of this study is on improving accuracy by feature selection. To this end, we propose two versions of ant colony optimization (ACO)-based algorithms for feature selection and show their effectiveness to improve the accuracy in detecting weld flaws and the accuracy in classifying weld flaw types. The performances of ACO-based methods are compared with that of no feature selection and that of sequential forward floating selection, which is a known good feature selection method. Four different classifiers, including nearest mean, k-nearest neighbor, fuzzy k-nearest neighbor, and center-based nearest neighbor, are employed to carry out the tasks of weld flaw identification and weld flaw type classification.