Tree Ensembles on the induced discrete space
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Dosyalar
Tarih
2016-05
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and these orderings are used as the new attributes in decision tree induction. Although K-tree performs significantly better than the proper one, their exponential time complexity can prohibit their use. In this brief, we propose K-forest, an extension of random forest, where a subset of features is selected randomly from the induced discrete space. Simulation results on 17 data sets show that the novel ensemble classifier has significantly lower error rate compared with the random forest based on the original feature space.
Açıklama
Anahtar Kelimeler
Classification, Decision trees, Feature extraction, Random forest, Artificial intelligence, Classification (of information), Learning system, Decision tree induction, Discrete attributes, Discrete spaces, Ensemble classifiers, Exponential time complexity, Predictive models, Tree ensembles
Kaynak
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
27
Sayı
5
Künye
Yıldız, O. T. (2016). Tree ensembles on the induced discrete space. IEEE Transactions on Neural Networks and Learning Systems, 27(5), 1108-1113. doi:10.1109/TNNLS.2015.2430277
