Experiments with Boosted Decision Tree Classifiers
Michał Woźniak · 2008
Boosting is the most popular method of improving quality and stabilizing weak classifiers. It bases on the voting by the group of classifiers, where each of them is generated on the basis of modified original learning set. The modification of AdaBoost.M1 and experimental results of boosted C4.5 (decision tree induction) algorithm are presented. All experimental researches are made on well known benchmark databases.