Neural Random Forests

Gérard Biau (), Erwan Scornet () and Johannes Welbl ()
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Gérard Biau: Sorbonne Université, CNRS, LPSM
Erwan Scornet: Centre de Mathématiques Appliquées, Ecole Polytechnique, CNRS
Johannes Welbl: University College London

Sankhya A: The Indian Journal of Statistics, 2019, vol. 81, issue 2, No 4, 347-386

Abstract: Abstract 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.

Keywords: Random forests; Neural networks; Ensemble methods; Randomization; Sparse networks.; Primary 62G08; Secondary 62G20, 68T05 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13171-018-0133-y

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