Bagging soft decision trees

Yükleniyor...
Küçük Resim

Tarih

2016

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Verlag

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

Organizasyon Birimleri

Dergi sayısı

Özet

The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both children with probabilities given by a sigmoid gating function. Hence for an input, all the paths to all the leaves are traversed and all those leaves contribute to the final decision but with different probabilities, as given by the gating values on the path. Tree induction is incremental and the tree grows when needed by replacing leaves with subtrees and the parameters of the newly-added nodes are learned using gradient-descent. We have previously shown that such soft trees generalize better than hard trees; here, we propose to bag such soft decision trees for higher accuracy. On 27 two-class classification data sets (ten of which are from the medical domain), and 26 regression data sets, we show that the bagged soft trees generalize better than single soft trees and bagged hard trees. This contribution falls in the scope of research track 2 listed in the editorial, namely, machine learning algorithms.

Açıklama

Anahtar Kelimeler

Bagging, Decision trees, Regression trees, Regularization

Kaynak

WoS Q Değeri

Q4

Scopus Q Değeri

Q3

Cilt

9605

Sayı

Künye

Yıldız, O. T., İrsoy, O. & Alpaydın, A. İ. E. (2016). Bagging soft decision trees. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9605, 25-36. doi:10.1007/978-3-319-50478-0_2