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

摘要： 提出了一种新的Boosting算法LAdaBoost。LAdaBoost算法利用局部错误率更新样本被选用于训练下一个分类器的概率，当对一个新的样本进行分类时，考虑了该样本与其邻域内的每个训练样本的近似度；另外，提出了有效邻域的概念。根据不同的组合方法，得到了两种LAdaBoost算法，即LAdaBoost-1和LAdaBoost-2。在UCI上部分实验数据集的实验结果表明，LAdaBoost算法比AdaBoost和Bagging算法更有效，且鲁棒性更好。

关键词: 机器学习, Bagging算法, Boosting算法, 噪声