A survey on machine learning for recurring concept drifting data streams. (1st March 2023)

• Record Type: Journal Article
• Authors: Suárez-Cetrulo, Andrés L.; Quintana, David; Cervantes, Alejandro
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Regime change -- Online machine learning -- Data streams -- Concept drift -- Meta learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118934
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time. Highlights: A comprehensive survey of techniques to deal with recurring changes in data streams. Review of recent approaches for model reuse and meta learning on data streams. Analysis of open challenges and future research trends.