Quantum mechanics

Reuse

Data stream mining

Generative model

Ecology

Pure mathematics

Biology

Event (particle physics)

Computer security

Concept drift

Field (mathematics)

Telecommunications

Data mining

Computer science

Computer network

Physics

Generative grammar

STREAMS

Mathematics

Data science

Artificial intelligence

Geometry

Cluster analysis

Point (geometry)

Machine learning

Data stream

Key (lock)

Expert Systems with Applications

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.

A survey on machine learning for recurring concept drifting data streams