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Using Validation to Avoid Overfitting in Boosting Using Validation to Avoid Overfitting in Boosting

AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because it focuses on misclassified examples, which may be noisy. We demonstrate that overfitting in AdaBoost can be alleviated in a time-efficient manner using a combination of dagging and validation sets. The training set is partitioned into subsets. Each subset is trained with AdaBoost generating multiple hypotheses. The hypotheses are then applied to the validation set, which is made up of the entire training set. The validation set adjusts the weights of the hypotheses. The hypotheses generated by all subsets are then aggregated with a weighted plurality vote for final classification. We show our algorithm has similar performance on standard datasets and improved performance when classification noise is added. We also apply validation sets to another subset training algorithm, the BB algorithm.