Learn R Programming

hyperSMURF (version 2.0)

Hyper-Ensemble Smote Undersampled Random Forests

Description

Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.

Copy Link

Version

Install

install.packages('hyperSMURF')

Monthly Downloads

58

Version

2.0

License

GPL (>= 2)

Maintainer

Giorgio Valentini

Last Published

April 29th, 2018

Functions in hyperSMURF (2.0)

do.stratified.cv.data.from.folds

Construction of folds for cross-validation from predefined folds
imbalanced.data.generator

Synthetic imbalanced data generator
smote

SMOTE oversampling
do.random.partition

Random partition of the data
do.stratified.cv.data

Construction of random folds for cross-validation
smote_and_undersample

SMOTE oversampling and undersampling
hyperSMURF.test

Test of a hyperSMURF model
hyperSMURF.cv

hyperSMURF cross-validation
hyperSMURF.test.thresh

Test of a thresholded hyperSMURF model
hyperSMURF.train

hyperSMURF training
hyperSMURF-package

hyperSMURF