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Handling Concept Drift

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Learning from Data Streams in Dynamic Environments

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

In this chapter, the different methods and techniques used to learn from data streams in evolving and nonstationary environments will be presented, and their performances will be compared according to the generated drift characteristics as well as to the application context and objectives. The goal is to define the criteria to be used in order to help readers to efficiently design the suitable learning scheme for a particular application. For this aim, these methods and techniques are classified and compared according to a set of meaningful criteria. Several examples will be used to illustrate and discuss the principal and the performance of these methods and techniques.

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Authors and Affiliations

  1. Computer Science and Automatic Control Department High National Engineering School of Mines, Douai, France

    Moamar Sayed-Mouchaweh

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  1. Moamar Sayed-Mouchaweh
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Sayed-Mouchaweh, M. (2016). Handling Concept Drift. In: Learning from Data Streams in Dynamic Environments. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-25667-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-25667-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25665-8

  • Online ISBN: 978-3-319-25667-2

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