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Statistical Methods for Imbalanced Data in Ecological and Biological Studies pp 45–55Cite as

Machine Learning Methods for Imbalanced Data

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Part of the book series: SpringerBriefs in Statistics ((JSSRES))

Abstract

We discuss high-dimensional data analysis in the framework of pattern recognition and machine learning, including single-component analysis and clustering analysis. Several boosting methods for tackling imbalances in sample sizes are investigated.

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Authors and Affiliations

  1. Seikei University, Musashino, Tokyo, Japan

    Osamu Komori

  2. The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan

    Shinto Eguchi

Authors
  1. Osamu Komori
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  2. Shinto Eguchi
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Correspondence to Osamu Komori .

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Komori, O., Eguchi, S. (2019). Machine Learning Methods for Imbalanced Data. In: Statistical Methods for Imbalanced Data in Ecological and Biological Studies. SpringerBriefs in Statistics(). Springer, Tokyo. https://doi.org/10.1007/978-4-431-55570-4_5

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