计算机工程与应用2018，Vol.54Issue(7)：132-137,6.DOI:10.3778/j.issn.1002-8331.1610-0308
抗外点干扰的鲁棒AdaBoost分类器构建方法
Robust AdaBoost classifier construction method against outlier interference
摘要
Abstract
Taking this reason that AdaBoost is sensitive to outliers,robust AdaBoost classifier is constructed against outlier interference using Ransac.Different from the other weak classifier sample weighting and controlling method in AdaBoost, Ransac is employed and introduced to the process of classifier model construction to overcome the drawbacks of the existing AdaBoost weak classifier weighting algorithms.Meanwhile,the adverse affection of outliers can be effectively eliminated by virtue of the Ransac algorithm's strong ability in removing outliers.Through above strategy,the classifier degradation is able to be avoided.Finally,in the validation experiment,the designed classifier model is applied in the handwriting samples classification including some outliers.The experimental results show its validity.关键词
AdaBoost分类器/Ransac算法/样本加权/分类Key words
AdaBoost classifier/Ransac algorithm/sample weighting/classification分类
信息技术与安全科学引用本文复制引用
曹万鹏,罗云彬,史辉..抗外点干扰的鲁棒AdaBoost分类器构建方法[J].计算机工程与应用,2018,54(7):132-137,6.基金项目
北京首批13所高校高精尖创新中心资助基金(No.PXM2016_014204_500072). （No.PXM2016_014204_500072）
