Data quality or data evaluation is sometimes a task as important as
collecting a large volume of data when it comes to generating accurate
artificial intelligence models. In fact, being able to evaluate the data can
lead to a larger database that is better suited to a particular problem because
we have the ability to filter out data obtained automatically of dubious
quality. In this paper we present RLBoost, an algorithm that uses deep
reinforcement learning strategies to evaluate a particular dataset and obtain a
model capable of estimating the quality of any new data in order to improve the
final predictive quality of a supervised learning model. This solution has the
advantage that of being agnostic regarding the supervised model used and,
through multi-attention strategies, takes into account the data in its context
and not only individually. The results of the article show that this model
obtains better and more stable results than other state-of-the-art algorithms
such as LOO, DataShapley or DVRL.

本文介绍了 RLBoost 算法，它使用深度强化学习策略评估数据集并获得一个能够估计任何新数据质量的模型，以提高监督学习模型的最终预测质量。该算法通过多关注策略，考虑了数据的上下文信息，与其他现有算法相比具有更好和更稳定的结果。