In this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification
of resistance spot welding joints. Instead of using the result of a non-destructive
testing technique as input variables, classifiers are trained directly with the
relevant welding parameters, i.e. welding current, welding time and the type of
electrode (electrode material and treatment). The algorithms are compared in
terms of accuracy and area under the receiver operating characteristic (ROC)
curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overall
