Out-of-bag样本的应用研究

Bagging集成通过组合不稳定的基分类器在很大程度上降低＂弱＂学习算法的分类误差,Out-of-bag样本是Bagging集成的自然产物。目前,Out-of-bag样本在估计Bagging集成的泛化误差、构建相关集成分类器等方面得到了广泛应用。文章对Out-of-bag样本的应用进行了综述,阐述了对其进行研究的主要内容和特点,并对它在将来可能的研究方向进行了讨论。

Research of the Applications of Out-of-bag Sample

Bagging can improve the classification error of a weak learning algorithm to a large extent through combining some instable base classifiers.Out-of-bag samples are naturally generated in the process of bagging.At present,out-of-bag samples haven been applied to many fields in ensemble learning,such as estimating the generalization error of a bagging ensemble,constructing ensemble classifiers and so on.This paper gives an overview of the applications of out-of-bag samples whose contents and main characteristics are illustrated.Meanwhile,the future research directions of out-of-bag samples are discussed.This specification is set for the theses to be published in Computer Software,including fonts,margins,page size and print area.