Download the full article
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 its objective is
to minimize error on the training set. We show that with the
introduction of a scoring function and the random selection of
training data it is possible to create a smaller set of feature
vectors. The selection of this subset of weak classifiers helps
boosting to reduce the generalization error and to avoid
overfitting on both synthetic and real data.
Index Terms: AdaBoost, Classifier, Overfitting, Feature selection.
 |
Luigi Rosa,
“A Fast Scheme for Feature Subset Selection to Avoid Overfitting in AdaBoost”,
February 2011.
|
