Neural Random Forests

Autor: Gérard Biau, Erwan Scornet, Johannes Welbl
Přispěvatelé: Laboratoire de Probabilités et Modèles Aléatoires (LPMA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Statistique Théorique et Appliquée (LSTA), Université Pierre et Marie Curie - Paris 6 (UPMC), University College of London [London] (UCL)
Rok vydání: 2018
Předmět:
FOS: Computer and information sciences
Statistics and Probability
Exploit
Computer science
Mathematics - Statistics Theory
Machine Learning (stat.ML)
Statistics Theory (math.ST)
randomization
sparse networks
Machine learning
computer.software_genre
01 natural sciences
Machine Learning (cs.LG)
010104 statistics & probability
03 medical and health sciences
Consistency (database systems)
0302 clinical medicine
Statistics - Machine Learning
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
FOS: Mathematics
0101 mathematics
Artificial neural network
business.industry
ensemble methods
Random forests
neural networks
Ensemble learning
Regression
Random forest
Variety (cybernetics)
Connection (mathematics)
Computer Science - Learning
Artificial intelligence
Statistics
Probability and Uncertainty
business
computer
030217 neurology & neurosurgery
Zdroj: Sankhya A. 81:347-386
ISSN: 0976-8378
0976-836X
DOI: 10.1007/s13171-018-0133-y
Popis: Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural random forests. Both predictors exploit prior knowledge of regression trees for their architecture, have less parameters to tune than standard networks, and less restrictions on the geometry of the decision boundaries than trees. Consistency results are proved, and substantial numerical evidence is provided on both synthetic and real data sets to assess the excellent performance of our methods in a large variety of prediction problems.
Databáze: OpenAIRE
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