自动化学报Issue(4)：635-642,8.DOI:10.3724/SP.J.1004.2014.00635
基于指数损失和0-1损失的在线Boosting算法
Online Boosting Algorithms Based on Exponential and 0-1 Loss
摘要
Abstract
In this paper, strict derivation for the online form of Boosting algorithms using exponential loss and 0-1 loss is presented, which proves that the two online Boosting algorithms can maximize the average margin and minimize the margin variance. By estimating the margin mean and variance incrementally, Boosting algorithms can be applied to online learning problems without losing classification accuracy. Experiments on UCI machine learning datasets show that the online Boosting using exponential loss is as accurate as batch AdaBoost, and significantly outperforms the traditional online Boosting, and that the online Boosting using 0-1 loss can minimize classification errors of positive samples and negative samples at the same time, thus applies to imbalance data. Moreover, Boosting using 0-1 loss is more robust on noisy data.关键词
AdaBoost/在线学习/特征选择/不平衡数据Key words
AdaBoost/online learning/feature selection/imbalance data引用本文复制引用
侯杰,茅耀斌,孙金生..基于指数损失和0-1损失的在线Boosting算法[J].自动化学报,2014,(4):635-642,8.基金项目
国家自然科学基金(60974129)资助@@@@Supported by National Natural Science Foundation of China (60974129) （60974129）
