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machine-learningsignal-processingclassificationweka

Using WEKA to get a classifier with a fixed sensitivity

I'm using WEKA to classify a certain dataset. In the results, I'm getting a Se=49% and Sp=99%. On observing the ROC, one can see that for Se=95%, Sp=88%. My question is that is there any way to adjust the classifier parameters (if any) so that my classifier is set to have a Se=95% and Sp=88% on the average.

PS: I'm using the Random Forest classifier in which the only parameters I input are # of trees, Max Depth (=0), # of features and seed.

Solution

  • On playing around with the ThresholdSelector in WEKA, I've managed to achieve my objective. By using Cost/Benefit analysis curve, I could find out the threshold value to suit my needs. Setting appropriate parameters in the ThresholdSelector then gave me the tuned classifier.