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2016 | OriginalPaper | Buchkapitel

An Overview of Concept Drift Applications

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Autoren: Indrė Žliobaitė, Mykola Pechenizkiy, João Gama

Verlag: Springer International Publishing

Erschienen in: Big Data Analysis: New Algorithms for a New Society

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Abstract

In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks. First we overview and categorize application tasks for which the problem of concept drift is particularly relevant. Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.

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Titel

An Overview of Concept Drift Applications

Buch

Big Data Analysis: New Algorithms for a New Society

Print ISBN: 978-3-319-26987-0

Electronic ISBN: 978-3-319-26989-4

https://doi.org/10.1007/978-3-319-26989-4

DOI

https://doi.org/10.1007/978-3-319-26989-4_4

Autoren:

Indrė Žliobaitė
Mykola Pechenizkiy
João Gama

Verlag

Springer International Publishing

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