In the TrAdaBoost algorithm, during every boosting iteration, a so-called weak classifier is built using weighted instance data from the previous iteration.
Five inhibitors appeared in every boosted training set, including the thiophene, cyclopentadiene and isoxazole analogs that fall outside the five major classes.
Then an AdaBoost process is performed on the labeled target data, and at every boosting iteration, the weak classifier used is selected from the candidate weak source classifiers (found in the previous step) that has the lowest classification error using the labeled target data.
In detail, the boosting procedure works as follows: For each covariate, one specific base-learner is defined and in every boosting step the algorithm updates only the covariate with the best performing base-learner.
In order to determine the optimal number of iterations, the classifier is evaluated on every boosting step by its area under the curve (AUC) performance (in a 3-fold cross-validation test).
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