Learning under Concept Drift: an Overview

TL;DR

This paper provides a comprehensive overview of concept drift in machine learning, discussing frameworks, adaptivity mechanisms, algorithms, related fields, and applications to give a broad understanding of the research landscape.

Contribution

It offers a structured overview and categorization of concept drift learners, formalizes the problem, and contextualizes research and applications in the field.

Findings

Categorizes concept drift algorithms based on properties

Provides formal framework for concept drift data

Maps research fields and applications

Abstract

Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.