A Survey on Classification of Concept Drift with Stream Data
Shweta Kadam · HAL (Le Centre pour la Communication Scientifique Directe) · 2019
Usually concept drift occurs in many applications of machine learning. Detecting a concept drift is the main challenge in a data stream because of the high speed and their large size sets which are not able to fit in main memory. Here we take a small look at types of changes in concept drift. This paper discusses about methods for detecting concept drift and focuses on the problems with existing approaches by adding STAGGER, FLORA family, Decision tree methods, meta-learning methods and CD algorithms. Furthermore, classifier ensembles for change detection are discussed.