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  • Mühendislik Fakültesi / Faculty of Engineering
  • Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
  • MF - Makale Koleksiyonu | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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  •   DSpace@Işık
  • 1- Fakülteler | Faculties
  • Mühendislik Fakültesi / Faculty of Engineering
  • Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
  • MF - Makale Koleksiyonu | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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Parallel univariate decision trees

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Date

2007-05-01

Author

Yıldız, Olcay Taner
Dikmen, Onur

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Citation

Yıldız, O. T. & Dikmen, O. (2007). Parallel univariate decision trees. Pattern Recognition Letters, 28(7), 825-832. doi:10.1016/j.patrec.2006.11.009

Abstract

Univariate decision tree algorithms are widely used in data mining because (i) they are easy to learn (ii) when trained they can be expressed in rule based manner. In several applications mainly including data mining, the dataset to be learned is very large. In those cases it is highly desirable to construct univariate decision trees in reasonable time. This may be accomplished by parallelizing univariate decision tree algorithms. In this paper, we first present two different univariate decision tree algorithms C4.5 and univariate linear discriminant tree. We show how to parallelize these algorithms in three ways: (i) feature based; (ii) node based; (iii) data based manners. Experimental results show that performance of the parallelizations highly depend on the dataset and the node based parallelization demonstrate good speedups.

Source

Pattern Recognition Letters

Volume

28

Issue

7

URI

https://hdl.handle.net/11729/257
http://dx.doi.org/10.1016/j.patrec.2006.11.009

Collections

  • MF - Makale Koleksiyonu | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering [63]
  • Scopus İndeksli Makale Koleksiyonu [885]
  • WoS İndeksli Makale Koleksiyonu [893]



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