I have a dataset with 28 attributes and 7 class values. I want to know if its possible to find out the most important attribute(s) for deciding the class value, for each class.
For example an answer could be: Attribute 2 is most important for class 1, Attribute 6 is most important for class 2 etc. Or an even more informed answer could be: Attribute 2 being below 0.5 is most important for class 1, Attribute 6 being above 0.75 is most important for class 2 etc
My initial approach to this was to build a decision tree on the data and find the node that had the largest information gain/gain ratio for each class and that would be the most determining factor for that class. The problem with this is that the decision tree implementations I have found don't give the information gain/gain ratio for each node and as this is time bound I don't have the time to implement my own version. My current thought is to create multiple datasets which are all one class vs the rest and then perform attribute selection (eg. information gain) on them to find the most important attribute. Is this the right direction to go down or is their a better option?