On COSMIC/TCGA RNA-seq data, Random Forest and Gradient Boosting classify 17 cancer types with 99.89% and 99.68% accuracy on a single 75/25 test split.
rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
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abstract
In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of only binary ones. Also, instead of using simple attribute subspace ensemble it employs bagging and thus produce error approximation and variable importance measure modelled after Random forest algorithm. I also present benchmarks' results which show that although Random ferns' accuracy is mostly smaller than achieved by Random forest, its speed and good quality of importance measure it provides make rFerns a reasonable choice for a specific applications.
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The efficacy of various machine learning models for multi-class classification of RNA-seq expression data
On COSMIC/TCGA RNA-seq data, Random Forest and Gradient Boosting classify 17 cancer types with 99.89% and 99.68% accuracy on a single 75/25 test split.