On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction

Abstract

Neural network algorithms such as those based on transformers and attention models have excelled on Automatic Text Classification (ATC) tasks. However, such enhanced performance comes at high computational costs. Ensembles of simpler classifiers (i.e., stacking) that exploit algorithmic and representational complementarities have also been shown to produce top-notch performance in ATC, enjoying high effectiveness and potentially lower computational costs. In this context, we present the first and largest comparative study to exploit the cost-effectiveness of stacking of ATC classifiers, consisting of transformers and non-neural algorithms. In particular, we are interested in answering research questions such as: Is it possible to obtain an effective ensemble with significantly less computational cost than the best learning model for a given dataset? Disregarding the computational cost, can an ensemble improve the effectiveness of the best learning model? Besides answering such questions, another main contribution of this paper is the proposal of a low-cost oracle-based method that can predict the best ensemble in each scenario (with and without computational cost limitations) using only a fraction of the available training data.