I have a highly imbalanced dataset and need to apply higher misclassfication costs to improve the minority class in my predictions. I am carrying out categorical binary classfication where i need to improve the false positive (minority class prediction).
I am aware that WEKA has this capability (cost senstive classfier) however i keep on getting error messages and was hoping there might be any other nodes out there which i could use.The error message i get is:
Execute failed: Length of probability estimates don't match cost matrix
If someone could post a workflow with a succesful use of these weka nodes with cost applied that would really help me too!
Additionally are there nodes out there which allow me to pick the features that i put into each level of my trees. I.e i want to always select feature 1 at the top of my trees as it might not be picked by algorithm for the first split of my ddecision tree. This is a type of interactive decision tree learner and was wondering if there were any nodes out there for this?
I was also thinking that prehaps i could use R integrated nodes to carry this out so any input on this would be helpful too!
Thank you for any help,