A context-aware machine learning-based approach.

It is known that training a general and versatile Machine Learning (ML)-based model is more cost-effective than training several specialized ML-models for different operating contexts. However, as the volume of training information grows, the higher the probability of producing biased results. Learning bias is a critical problem for many applications, such as those related to healthcare scenarios, environmental monitoring and air traffic control. In this paper, we compare the use of a general model that was trained using all contexts against a system that is composed of a set of specialized models that was trained for each particular operating context. For this purpose, we propose a local learning approach based on context-awareness, which involves: (i) anticipating, analyzing and representing context changes; (ii) training and finding machine learning models to maximize a given scoring function for each operating context; (iii) storing trained ML-based models and associating them with corresponding operating contexts; and (iv) deploying a system that is able to select the best-fit ML-based model at runtime based on the context. To illustrate our proposed approach, we reproduce two experiments: one that uses a neural network regression-based model to perform predictions and another one that uses an evolutionary neural network-based approach to make decisions. For each application, we compare the results of the general model, which was trained based on all contexts, against the results of our proposed approach. We show that our context-aware approach can improve results by alleviating bias with different ML tasks.