The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap or cross-validation methods.



Titolo: ROSE: a Package for Binary Imbalanced Learning
Autori: 
MENARDI, GIOVANNA
TORELLI, Nicola
mostra contributor esterni 
Data di pubblicazione: 2014
Rivista: 
THE R JOURNAL  
Abstract: The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap or cross-validation methods.
Handle: http://hdl.handle.net/11368/2787725
URL: http://journal.r-project.org/archive/2014-1/menardi-lunardon-torelli.pdf
Appare nelle tipologie:1.1 Articolo in Rivista

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