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High-Dimensional Data Classification

In: Clusters, Orders, and Trees: Methods and Applications

Author

Listed:
  • Vijay Pappu

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Recently, high-dimensional classification problems have been ubiquitous due to significant advances in technology. High dimensionality poses significant statistical challenges and renders many traditional classification algorithms impractical to use. In this chapter, we present a comprehensive overview of different classifiers that have been highly successful in handling high-dimensional data classification problems. We start with popular methods such as Support Vector Machines and variants of discriminant functions and discuss in detail their applications and modifications to several problems in high-dimensional settings. We also examine regularization techniques and their integration to several existing algorithms. We then discuss more recent methods, namely the hybrid classifiers and the ensemble classifiers. Feature selection techniques, as a part of hybrid classifiers, are introduced and their relative merits and drawbacks are examined. Lastly, we describe AdaBoost and Random Forests in the ensemble classifiers and discuss their recent surge as useful algorithms for solving high-dimensional data problems.

Suggested Citation

  • Vijay Pappu & Panos M. Pardalos, 2014. "High-Dimensional Data Classification," Springer Optimization and Its Applications, in: Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), Clusters, Orders, and Trees: Methods and Applications, edition 127, pages 119-150, Springer.
    • Handle: RePEc:spr:spochp:978-1-4939-0742-7_8
    DOI: 10.1007/978-1-4939-0742-7_8
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    Cited by:

    1. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.

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