一种用于分类的改进Boosting算法  

Improved Boosting algorithm for classification problems

作　　者：刘凯[1] 王正群[2] 

机构地区：[1]江苏畜牧兽医职业技术学院,江苏泰州225300 [2]扬州大学信息工程学院,江苏扬州225009

出　　处：《计算机工程与应用》2012年第6期146-150,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60875004);江苏省自然科学基金(No.BK2009184);江苏省高校自然科学基础研究资助项目(No.07KJB520133)

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,LAda-Boost算法比AdaBoost和Bagging算法更有效,且鲁棒性更好。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.

关 键 词：机器学习 BAGGING算法 BOOSTING算法 噪声 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]