RLBoost: Boosting Supervised Models using Deep Reinforcement Learning
Eloy Anguiano Batanero Ángela Fernández Pascual Álvaro Barbero Jiménez
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.