Parallel univariate decision trees

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Tarih

2007-05-01

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier B.V.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

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Özet

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.

Açıklama

Anahtar Kelimeler

Decision trees, Parallel processing, Univariate decision trees, Linear discriminant trees, Algorithms, Data mining, Data structures, Decision theory, Parallel processing systems, Parallelization, Trees (mathematics)

Kaynak

Pattern Recognition Letters

WoS Q Değeri

Q2
Q2

Scopus Q Değeri

Q1

Cilt

28

Sayı

7

Künye

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