Fusion Classifier in Weka?

Question

I have a dataset with 20 features. 10 for age and 10 for weight. I want to classify the data for both separately then use the results from these 2 classifiers as an input to a third for the final result..

Is this possible with Weka????

Answer 1

Fusion of decisions is possible in WEKA (or with any two models), but not using the approach you describe.

Seeing as your using classifiers, each model will only output a class. You could use the two labels produced as features for a third model, but the lack of diversity in your inputs would most likely prevent the third model from giving you anything interesting.

At the most basic level, you could implement a voting scheme. Give each model a "vote" and then take assume that the correct class is the majority voted class. While this will give a rudimentary form of fusion, if you're familiar with voting theory you know that majority-rules somewhat falls apart when you have more than two classes.

I recommend that you use Combinatorial Fusion to fuse the output of the two classifiers. A good paper regarding the technique is available as a free PDF here. In essence, you use the Classifer::distributionForInstance() method provided by WEKA's classifiers and then use the sum of the distributions (called "scores") to rank the classes, choosing the class with the highest rank. The paper demonstrates that this method is superior to doing just voting alone.