R "Pool" support vector machines for subsetting data sets - r

Question

Facts for svm:
positve data set 20 samples, 5 factors
negative data set 10000 samples, 5 factors
package: e1071 or kernel
My test dataset would be something like 15000 samples
To control this imbalance i tried to use the class weight in e1071, as suggested in previous questions.But i cannot see any differences also whole overweighting one class extremely.
Now i was thinking to subset my negative data set randomly in 100 sub negative datasets. Like this
cost<-vector("numeric", length(1))
gamma <- vector("numeric", length(1))
accuracy<- vector("numeric" , length(1)
)
Function definition
split_data<- function(x,repeats) {
for (i in 1:repeats){
random_data <- x[sample(1:nrow(x), 100),]
dat<- rbind(data_pos, random_data)
svm <- svm(Class~., data=dat, cross=10)
cost[i] <- svm$cost
gamma[i] <-svm$gamma
accuracy[i]<- svm$tot.accuracy
print(summary(svm))
}
return(matrix(c(cost,gamma,accuracy), ncol=3))
}
But Im not sure what to do now with ... :D Its seems to define always the same support vectors in my pos data set. But there should be a smarter strategy, i have read about some strategies but is it possible to realize them in R with any package?
Edit:
I would like to find an approach how i can deal with highly imbalanced datasets.And i would like to do it in this way: to split my negative data set (resampled test data set) in equal portions to my positive dataset. However I would then somehow like to get the complete accuracy and sensitivity.
My question in particular is: how can i manage this in R a nice way?
Thanks a lot