One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments

TL;DR

This survey reviews methods for detecting and localizing concept drift in unsupervised data streams, emphasizing their importance in monitoring evolving environments and anomaly detection, and provides systematic comparisons and guidelines.

Contribution

It offers the first comprehensive review of unsupervised concept drift detection, including a taxonomy, mathematical definitions, standardized experiments, and insights on explaining drift.

Findings

Systematic comparison of detection strategies

Guidelines for real-world application

Introduction of drift explanation approaches

Abstract

The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavior, which may be safety-critical in many scenarios, detecting and analyzing concept drift is crucial. In this paper, we provide a literature review focusing on concept drift in unsupervised data streams. While many surveys focus on supervised data streams, so far, there is no work reviewing the unsupervised setting. However, this setting is of particular relevance for monitoring and anomaly detection which are directly applicable to many tasks and challenges in engineering. This survey provides a taxonomy of existing work on drift detection. Besides, it covers the current state of research on drift localization in a systematic way. In addition to providing a systematic literature review, this work provides precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different strategies for detection and localization. Thereby, the suitability of different schemes can be analyzed systematically and guidelines for their usage in real-world scenarios can be provided. Finally, there is a section on the emerging topic of explaining concept drift.