计算机工程与应用2012，Vol.48Issue(6)：146-150,5.DOI:10.3778/j.issn.1002-8331.2012.06.043
一种用于分类的改进Boosting算法
Improved Boosting algorithm for classification problems
摘要
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 Ada-Boost and Bagging.关键词
机器学习/Bagging算法/Boosting算法/噪声Key words
machine learning/Bagging/Boosting/noise分类
信息技术与安全科学引用本文复制引用
刘凯,王正群..一种用于分类的改进Boosting算法[J].计算机工程与应用,2012,48(6):146-150,5.基金项目
国家自然件学基金(No.60875004) （No.60875004）
江苏省自然科学基金(No.BK2009184) （No.BK2009184）
江苏省高校自然科学基础研究资助项目(No.07KJB520133). （No.07KJB520133）
