Regularizing soft decision trees
Yükleniyor...
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recently, we have proposed a new decision tree family called soft decision trees where a node chooses both its left and right children with different probabilities as given by a gating function, different from a hard decision node which chooses one of the two. In this paper, we extend the original algorithm by introducing local dimension reduction via L-1 and L-2 regularization for feature selection and smoother fitting. We compare our novel approach with the standard decision tree algorithms over 27 classification data sets. We see that both regularized versions have similar generalization ability with less complexity in terms of number of nodes, where L-2 seems to work slightly better than L-1.
Açıklama
Anahtar Kelimeler
Algorithms, Classification (of information), Data mining, Information science, Decision-tree algorithm, Dimension reduction, Gating functions, Generalization ability, Hard decisions, Original algorithms, Decision trees
Kaynak
Lecture Notes in Electrical Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
264
Sayı
Künye
Yıldız, O. T. & Alpaydın, A. İ. E. (2014). Regularizing soft decision trees. Paper presented at the Lecture Notes in Electrical Engineering, 264, 15-21. doi:10.1007/978-3-319-01604-7_2
