View article

Stream data learning is an emerging machine learning topic, and it has many challenges. One of its challenges is the dynamic behavior or changes in the environment which leads to drifts. Two types of drift occur, namely, concept drift and feature drift. This article provides a survey on stream data learning with focusing on the issues of feature drift and the methods developed for handling it. After presenting the fundamental concepts and definition in this field, it provides an overview of the various models and methods developed for detecting feature drift and maintaining the validity of the machine learning models when the drift occurs. Furthermore, the article provides the generators used for creating dataset with feature drift to provide benchmarking for approaches of detecting or handling feature drift. The article provides also taxonomy of feature selection methods in both static and dynamic environment. It concludes that reinforcement-based models are promising for this task, and it lists various open challenges and future works in this area.