How to automatically optimize a classifier in Weka in order to have a given class to contain 100 % sure data?
I have two (or three) classes and each classes can only possess one label.
I want to optimize (automatically if possible) parameters and thresholds of classifiers in order for my first class to contain only 100 % sure data. Even if it contains a small number of instances.
I don't mind for the remaining classes to contain false alarm or correct rejection.
I don't mind to have unclassified data.
I have already been searching on stackoverflow and on the weka's wiki but maybe my lack of knowledge concerning weka made me miss some keywords.
I also tried to perform the task with the well-known "iris" database but I think that in this case, any class can be 100 % sure.
Yet, I have only succeed in testing multiple classifiers and tuning them manually but without performing 100 % correct for my first class. (I checked this result in the confusion matrix given by weka's report.) Somehow, I know it is possible for my class to contain 100% sure data because I managed to do it in Matlab with simple threshold set manually. But I would like to try out a bigger database, to obtain better threshold and to use the power of weka.
Any suggestions would be helpful, thanks !
You probably need the "Cost Sensitive Classifier" among "meta" classifiers. If you are working in the Explorer, here is the dialog you get.
Choose the your "classifier" (something beyond ZeroR :) ). Set your "cost matrix". For 2-class problem this will be 2x2 matrix. By setting one non-diagonal component very large (>>1, let us say 1000) you ensure that misclassifying one class (your "first" class) is 1000 times more expensive than misclassifying another class. This should do the job.
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