Poor performance shown on Rare event modeling
If you are willing to use the caret package in R and use random forests, you can use the method in the following blog post for downsampling with unbalanced datasets: http://appliedpredictivemodeling.com/blog/2013/12/8/28rmc2lv96h8fw8700zm4nl50busep

Basically, you just add a single line to your train call. Here is the relevant part: 

&gt; rfDownsampled &lt;- train(Class ~ ., data = training,
+                        method = "rf",
+                        ntree = 1500,
+                        tuneLength = 5,
+                        metric = "ROC",
+                        trControl = ctrl,
+                        ## Tell randomForest to sample by strata. Here, 
+                        ## that means within each class
+                        strata = training$Class,
+                        ## Now specify that the number of samples selected
+                        ## within each class should be the same
+                        sampsize = rep(nmin, 2))


I have had some success with this approach in your type of situation.

For some more context, here is an in-depth post about experiments with unbalanced datasets: http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/