Robustness of Raw Images Classifiers Against the Class Imbalance - A Case Study

摘要

Our aim is to investigate the robustness of classifiers against the class imbalance. From this point of view, we compare several most widely used classifiers as well as the one recently proposed, which is based on the assumption that the probability densities in classes have the matrix normal distribution. As the base for comparison we take a sequence of images from that laser based additive manufacturing process. It is important that the classifiers are fed by raw images. The classifiers are compared according to several criterions and the methodology of all pair-wise comparisons is used to rank them.