Work

Synthetic learning machines

Year: 2014

Type: article

Abstract: BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Source: BioData Mining

DOI: 10.1186/s13040-014-0028-y

Authors: Hemant Ishwaran, James D Malley

Institutions: University of Miami, National Institutes of Health, Center for Information Technology

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Science Foundation, National Institutes of Health, National Cancer Institute