I have generated an adaboost classifier in Weka on a dataset where each instance falls into one of two classes. The result was a number of decision trees, each assigned a weight.
What is the proper method for implementing the classifier generated by adaboost? I assumed the answer was
(Weight of Tree 1 * decision of tree1) + (weight of tree 2 * decision of tree 2) ... + (weight of tree n * decision of tree n)
Where each tree will decide if the instance falls into class A (returning +1) or class B (returning -1)
If the final result sum of weight*result is positive the instance is class A, if negative, class B.
The problem is when I implemented exactly this the results are nowhere near what Weka produced, so I assume that this was not the correct way to implement the classifier.
What should I have done instead?