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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Dealing with Data Difficulty Factors While Learning from Imbalanced Data

verfasst von : Jerzy Stefanowski

Erschienen in: Challenges in Computational Statistics and Data Mining

Verlag: Springer International Publishing

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Abstract

Learning from imbalanced data is still one of challenging tasks in machine learning and data mining. We discuss the following data difficulty factors which deteriorate classification performance: decomposition of the minority class into rare sub-concepts, overlapping of classes and distinguishing different types of examples. New experimental studies showing the influence of these factors on classifiers are presented. The paper also includes critical discussions of methods for their identification in real world data. Finally, open research issues are stated.

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Metadaten
Titel
Dealing with Data Difficulty Factors While Learning from Imbalanced Data
verfasst von
Jerzy Stefanowski
Copyright-Jahr
2016
Buch
Challenges in Computational Statistics and Data Mining

Print ISBN: 978-3-319-18780-8

Electronic ISBN: 978-3-319-18781-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-18781-5_17