Title: Using Random Forest to Learn Imbalanced Data
Author: Chao Chen, Andy Liaw and Leo Breiman
Date: July 2004
Pub: PDF
Url: http://www.stat.berkeley.edu/users/chenchao/666.pdf

Abstract:

In this paper we propose two ways to deal with the imbalanced data
classification problem using random forest. One is based on cost
sensitive learning, and the other is based on a sampling
technique. Performance metrics such as precision and recall, false
positive rate and false negative rate, $F$-measure and weighted
accuracy are computed. Both methods are shown to improve the
prediction accuracy of the minority class, and have favorable
performance compared to the existing algorithms.