Experimental study of rehearsal-based incremental classification of document streams

This research work proposes a novel protocol for rehearsal-based incremental learning models for the classification of business document streams using deep learning and, in particular, transformer-based natural language processing techniques. When implementing a rehearsal-based incremental classification model, the questions raised most often for parameterizing the model relate to the number of instances from “old” classes (learned in previous training iterations) which need to be kept in memory and the optimal number of new classes to be learned at each iteration. In this paper, we propose an incremental learning protocol that involves training incremental models using a weight-sharing strategy between transformer model layers across incremental training iterations. We provide a thorough experimental study that enables us to determine optimal ranges for various parameters in the context of incremental classification of business document streams. We also study the effect of the order in which the classes are presented to the model for learning and the effects of class imbalance on the model’s performances. Our results reveal no significant difference in the performances of our incrementally trained model and its statically trained counterpart after all training iterations (especially when, in the presence of class imbalance, the most represented classes are learned first). In addition, our proposed approach shows an improvement of 1.55% and 3.66% over a baseline model on two business documents dataset. Based on this experimental study, we provide a list of recommendations for researchers and developers for training rehearsal-based incremental classification models for business document streams. Our protocol can be further re-used for other final applications.