关键词: 机器学习, Bagging算法, Boosting算法, 噪声

Abstract: A new Boosting algorithm named LAdaBoost is proposed, which utilizes a local error to update the probability that the instance is selected to be part of next classifier’s training set. When classifying a new instance, the similarity between the instance and each training instance in its neighborhood is taken into account. Furthermore, the concept of effective neighborhood is first given. According to different combination methods, it gets two LAdaBoost algorithms LAdaBoost-1 and LAdaBoost-2. The experimental results on several datasets available from the UCI repository demonstrate that LAdaBoost algorithms are more robust and efficient than AdaBoost and Bagging.

Key words: machine learning, Bagging, Boosting, noise