Introduction:
Algorithm: Adaboost. A boosting algorithm—creates an ensemble of classifiers. Each one gives a weighted vote.
Input:
Output: A composite model.
Method:
(1) initialize the weight of each tuple in D to 1=d;
(2) for i = 1 to k do // for each round:
(3) sample D with replacement according to the tuple weights to obtain Di;
(4) use training set Di to derive a model, Mi;
(5) compute error(Mi), the error rate of Mi (Equation 6.66)
(6) if error(Mi) > 0:5 then
(7) reinitialize the weights to 1=d
(8) go back to step 3 and try again;
(9) endif
(10) for each tuple in Di that was correctly classified do
(11) multiply the weight of the tuple by error(Mi)=(1-error(Mi)); // update weights
(12) normalize the weight of each tuple;
(13) endfor
To use the composite model to classify tuple, X:
(1) initialize weight of each class to 0;
(2) for i = 1 to k do // for each classifier:
(3) wi = log 1-error(Mi)/error(Mi) ; // weight of the classifier’s vote
(4) c = Mi(X); // get class prediction for X from Mi
(5) add wi to weight for class c
(6) endfor
(7) return the class with the largest weight;
