中南大学学报（自然科学版）2012，Vol.43Issue(2)：561-566,6.
基于不确定性采样的自训练代价敏感支持向量机研究
Self-training cost-sensitive support vector machine with uncertainty based on sampling
摘要
Abstract
Self-training cost-sensitive support vector machine with uncertainty based sampling (SCU) was proposed to solve two difficulties of class-imbalanced dataset and expensive labeled cost. The uncertainty of unlabeled sample was evaluated using support vector data description in uncertainty based sampling. The unlabeled sample with high uncertainty was selected to be labeled. Cost-sensitive support vector machine was trained using self-training approach. Cost parameters and kernel parameters of cost-sensitive support vector machine were employed to predict a class label for an unlabeled sample. The results show that SCU effectively reduces both average expected misclassification costs and labeled times.关键词
主动学习/代价敏感支持向量机/自训练方法/不确定性采样/支持向量数据描述Key words
active learning/ cost-sensitive support vector machine/ self-training approach/ uncertainty based sampling/ support vector data description分类
通用工业技术引用本文复制引用
江彤,唐明珠,阳春华..基于不确定性采样的自训练代价敏感支持向量机研究[J].中南大学学报（自然科学版）,2012,43(2):561-566,6.基金项目
国家杰出青年科学基金资助项目(61025015) （61025015）
国家自然科学基金资助项日(60874069) （60874069）
国家高技术研究发展计划(“863”计划)项目(2009AA04Z137) （“863”计划）
湖南省教育厅科学研究项目(11C0699) （11C0699）
中南大学优秀博士学位论文扶植项目(2010年) （2010年）
