Parallel univariate decision trees
Yükleniyor...
Tarih
2007-05-01
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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
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
