AdaBoost Algorithm with Classification Belief
Yan Cha · 2015
Ensemble learning is widely accepted and used in machine learning. This paper proposes a multi-class ensemble learning algorithm named AdaBoost belief. The algorithm improves AdaBoost·SAMME by attaching weights to classes in every weak classifier. These weights, called class beliefs, are computed based on class accuracy collected in each round of the iteration. We compare the algorithm with AdaBoost·SAMME in many aspects including learning accuracy, generalization ability, and theory support. Experimental results indicate that the proposed method has a competitive learning ability and high prediction accuracy in Gaussian sets, several UCI sets, anda number of log-based intrusion detection applications. When the class number increases so that prediction of classes becomes more difficult, the prediction error rate of the proposed algorithm increases slower than AdaBoost·SAMME.