A reinforcement learning approach for single redundant view co-training text classification

Abstract

We tackle the problem of learning classification models with very small amounts of labeled data (e.g., less than 10% of the dataset) by introducing a novel Single View Co-Training strategy supported by Reinforcement Learning (CoRL). CoRL is a novel semi-supervised learning framework that can be used with a single view (representation). Differently from traditional co-training that requires at least two sufficient and independent data views (e.g., modes), our solution is applicable to any kind of data. Our approach exploits a reinforcement learning (RL) paradigm as a strategy to relax the view independence assumption, using a stronger iterative agent that builds more precise combined decision class boundaries. Our experimental evaluation with four popular textual benchmarks demonstrates that CoRL can produce better classifiers than confidence-based co-training methods, while producing high effectiveness in comparison with the state-of-the-art in semi-supervised learning. In our experiments, CoRL reduced the labeling effort by more than 80% with no losses in classification effectiveness, outperforming state-of-the-art baselines, including methods based on neural networks, with gains of up to 96% against some of the best competitors.