Hi I am developing a fraud prediction model. Because this is a highly unbalanced classification problem I have chosen to try to resolve it by Random Forests.

Inspired by this article
http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf
I have chosen to try Balanced Random Forests.

For now I am not completely sure how to implement these Forests in R.
The article suggests that: For each iteration in random forest, draw a bootstrap sample from the minority class.
Randomly draw the same number of cases, with replacement, from the majority class.

Is this achieved by specifying these parrameters?

replace = TRUE  
strata = fraud.variable  
sampsize = c(x,x) where x is the size of samples to be drawn

Hi I am developing a fraud prediction model. Because this is a highly unbalanced classification problem I have chosen to try to resolve it by Random Forests.

Inspired by this article
http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf
I have chosen to try Balanced Random Forests.

For now I am not sure how to implement these Forests in R.
The article suggests that: For each iteration in random forest, draw a bootstrap sample from the minority class.
Randomly draw the same number of cases, with replacement, from the majority class.

Is this achieved by specifying these parameters?

replace = TRUE  
strata = fraud.variable  
sampsize = c(x,x) where x is the size of samples to be drawn