Learning under Concept Drift: an Overview

By Indrė Žliobaitė. arXiv.org, 2010

485 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2075c51f185b5bd200e804e4490a80b2853cdb36

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Citations in the corpus

Performative Prediction (2020, background): “Importantly, this line of work also discusses the importance of retraining [35, 10].”
Fraud detection system: A survey (2016, background): “5 (Wang et al., 2003; Zliobaite, 2010; Ross et al., 2012).”
Analyzing concept drift and shift from sample data (2018, methodology): “Prior techniques for describing concept drift (Widmer and Kubat, 1996; Kifer et al, 2004; Gama et al, 2004; Baena-Garcıa et al, 2006; Nishida and Yamauchi, 2007; Dries and Rückert, 2009; Gama and Rodrigues, 2009; Žliobaite, 2010; Hoens et al, 2012; Moreno-Torres et al, 2012; Bifet et al, 2013; Gama ”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “In the case of passive approaches the classifier is continuously updated as soon as new supervised samples become available (no triggering mechanism involved) [18, 157, 158].”
Detecting concept drift in data streams using model explanation (2018, background): “…classiﬁcation are available (Gama et al., 2014; Tsymbal, 2004; Masud et al., 2011), but they all share the common principles of operations - handling data in the face of strict temporal and spatial constraints and implementing some mechanism of change detection and adaptation (ˇZliobait˙e, 2010).”