Synthetic learning machines

Hemant Ishwaran, James D. Malley

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Using a collection of different terminal nodesize constructed random forests, each generating a synthetic feature, a synthetic random forest is defined as a kind of hyperforest, calculated using the new input synthetic features, along with the original features. Using a large collection of regression and multiclass datasets we show that synthetic random forests outperforms both conventional random forests and the optimized forest from the regresssion portfolio. Synthetic forests removes the need for tuning random forests with no additional effort on the part of the researcher. Importantly, the synthetic forest does this with evidently no loss in prediction compared to a well-optimized single random forest.

Original languageEnglish (US)
Article number28
JournalBioData Mining
Volume7
Issue number1
DOIs
StatePublished - Jan 1 2014

Keywords

  • Machine
  • Nodesize
  • Random forest
  • Synthetic feature
  • Trees

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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