一种基于旋转森林的集成协同训练算法

集成协同训练算法（ensemble co-training）是将集成学习（ensemble learning）和协同训练算法（co-training）相结合的半监督学习方法，旋转森林（rotation forest）是利用特征提取来构造基分类器差异性的集成学习方法，在对现有的集成协同训练算法研究基础上，提出了基于旋转森林的协同训练算法——ROFCO，该方法重在利用未标记数据提高基分类器之间的差异性和特征提取效果，使基分类器的泛化误差保持不变或下降的同时，能保持甚至提高基分类器之间的差异性，提高集成效果。实验结果表明该方法能取得较好效果。

Ensemble co-training algorithm based on rotation forest

Ensemble co-training is a kind of semi-supervised learning method which combines ensemble learning and co-training.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 ...