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Learning from Imbalanced Data Streams

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Learning from Imbalanced Data Sets

Abstract

Mining data streams is one of the most vital fields in the contemporary ML. Increasing number of real-world problems are characterized by both volume and velocity of data, as well as by evolving characteristics. Learning from data stream assumes that new instances arrive continuously and that their properties may change over time due to a phenomenon known as concept drift. In order to achieve good adaptation to such non-stationary problems, classifiers must not only be accurate and able to continuously accommodate new instances, but also be characterized by high speed and low computational costs. A very challenging subfield of this domain is imbalanced data stream mining. It combined difficulties from streaming and imbalanced data, as well as introduce a plethora of new ones. Algorithms designed for such scenarios must be flexible enough to quickly adapt to changing decision boundaries, imbalance ratios, and roles of classes. In this chapter we will discuss the basics of data stream mining methods, as well as review existing skew-insensitive algorithms. Background in data streams is given in Sect. 11.1. Section 11.2 discusses in-depth learning difficulties present in imbalanced data streams. Data-level and algorithm level methods for skewed data streams are discussed in Sect. 11.3, while ensemble learners are overview in Sect. 11.4. Section 11.5 concentrates on issue of emerging and disappearing classes, while Sect. 11.6 deals with the limited access to ground truth in streaming scenarios. Finally, Sect. 11.7 concludes this chapter and presents future challenges in the field of learning from imbalanced data streams.

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Authors and Affiliations

  1. Department of Computer Science and AI, University of Granada, Granada, Granada, Spain

    Alberto Fernández & Salvador García

  2. Institute of Smart Cities, Public University of Navarre, Pamplona, Spain

    Mikel Galar

  3. Department of Computer Science, Universidade Federal do ABC, Santo Andre, Brazil

    Ronaldo C. Prati

  4. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

    Bartosz Krawczyk

  5. Department of Computer Science and AI, University of Granada, Granada, Spain

    Francisco Herrera

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  1. Alberto Fernández
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  2. Salvador García
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  3. Mikel Galar
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  4. Ronaldo C. Prati
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  5. Bartosz Krawczyk
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  6. Francisco Herrera
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Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F. (2018). Learning from Imbalanced Data Streams. In: Learning from Imbalanced Data Sets. Springer, Cham. https://doi.org/10.1007/978-3-319-98074-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-98074-4_11

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