Improve Recall for certain classes / Rescall Confidences multiclass classification
I' m working on a classification model to predict multiple classes. Concerning i have two questions.
1) I there a way to force the algorithm/model to focus only on some classes and to improve the recall probabilities for those classes? I have learned the "Threshold"-operators are only usable for binominal classification problems. Another way would be to use the “Meta Cost”- Operator, but about the high number of classes (around 30), i would be prefer a more automated way. Do you have any ideas how i can handle this issue?
2) I build up a classification model with the "Fast Large Margin"-algorithm nested in the "Polynomial by Binomial Classification"-Operator. The prediction results sum up the confidences for the different classes. From my knowledge, these confidences contain the distance to the separating hyperplane. Is there a way to calculate the probabilities for each class?
I know for binominal classification problems the “Rescale Confidences”- Operator can be used and LibSVM as another SVM method provides the possibility to estimate the probabilities. However, in due to better results with the Fast Large Margin algorithm I would like to calculate these probabilities explicitly. Do you know a solution how to build up this process in RapidMiner?
Thanks in advance