计算机工程与应用2011，Vol.47Issue(30)：172-175,4.DOI:10.3778/j.issn.1002-8331.2011.30.047
一种基于旋转森林的集成协同训练算法
Ensemble co-training algorithm based on rotation forest
摘要
Abstract
Ensemble co-training is a kind of semi-supervised learning method which combines ensemble learning and co-train-ing.Rotation forest is a kind of ensemble learning which generates classifier ensembles based on feature extraction.A novel ensemble co-training algorithm which named ROFCO is proposed which focuses on using unlabeled data to improve the diversity between base classifiers and feature extraction effect.The base classifiers will maintain or decrease the generalization error, while maintaining or even improving diversity between them.Experiments on UC1 data set and the benchmark data set demonstrate that compared with other similar algorithms ROFCO could get much better performance.关键词
集成协同训练/旋转森林/差异性/特征提取/旋转森林的协同训练方法(ROFCO)Key words
ensemble co-training/ rotation forest/diversity/ feature extraction/ Rotation Forest Co-training (ROFCO)分类
信息技术与安全科学引用本文复制引用
刘敏,谢伙生..一种基于旋转森林的集成协同训练算法[J].计算机工程与应用,2011,47(30):172-175,4.基金项目
福建省教育厅基金(No.JB07023) （No.JB07023）
福州大学科技发展基金(No.2006—XQ-22). （No.2006—XQ-22）
