Univariate decision tree induction using maximum margin classification
Yükleniyor...
Tarih
2012-03
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Oxford Univ Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. For two-class data sets, it generates significantly smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass data sets with one-vs-rest methodology.
Açıklama
Anahtar Kelimeler
Computer Science, Statistical learning theory, Decision trees
Kaynak
Computer Journal
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
55
Sayı
3
Künye
Yıldız, O. T. (2012). Univariate decision tree induction using maximum margin classification. Computer Journal, 55(3), 293-298. doi:10.1093/comjnl/bxr020
