Method for training a classifier and system for classifying blocks

Abstract

Blocks of spatially structured information such as log files or images are processed in a training loop for an attention-based classifier, using an active learning approach. First, the classifier provides a predicted label and an attention map for each classified block. Blocks are selected from the classified blocks if the output of the classifier for the respective block meets a selection criterion. The selected blocks are then displayed to a user together with the predicted label and a visual representation of the attention map. Based on these changes, the classifier is retrained. The method allows for an automatic, intelligent selection of a small number of data points that need to be labeled by a domain expert. The domain expert does not need to collect the training data a priori, but systematically and iteratively gets asked for training examples that are then directly used by the machine learning algorithm for learning.