High-Dimensional Data Classification

Vijay Pappu () and Panos M. Pardalos
Additional contact information
Vijay Pappu: University of Florida
Panos M. Pardalos: University of Florida

A chapter in Clusters, Orders, and Trees: Methods and Applications, 2014, pp 119-150 from Springer

Abstract: 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.

Keywords: High dimensional data classification; Ensemble methods; Feature selection; Curse of dimensionality; Regularization (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4939-0742-7_8

Ordering information: This item can be ordered from
http://www.springer.com/9781493907427

DOI: 10.1007/978-1-4939-0742-7_8

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().