文档介绍：.页眉. .页脚. 基于半监督学习的数据流分类方法黄树成 1,2 朱宇光 2 董逸生 1 (东南大学计算机科学与工程学院,南京 210096) (常州工学院计算机科学与工程系,江苏常州 213006) ( ******@so h )摘要在数据流上建立有效的分类模型具有许多应用。流数据的动态性给业界提出了两个关键问题:如何监测数据流的变化;一旦发生显著变化, 如何高效地组织足够的训练数据, 调整无效的模型。本文提出了一个基于半监督学习的分类算法,较好地解决了这些问题。设计了一种识别显著变化的可靠方法;提出了一个基于 Na? ve Bayes 的 EM算法,利用较少的类标数据来扩大训练数据集,从而极大地降低类标数据的需求量。基于新的训练数据可以构建一个有效的分类器。实验结果证明了算法的优势。关键词数据流分类;显著变化;类标数据;半监督学习;基于 Na? ve Bayes 的 EM算法。中图法分类号 Semi-supervised Learning Based Approach for Classifying Data Streams H uang Shu c heng 1,2D ony Yis heng 1 1 (School puter Science and Engineering, Southeast University, Nanjing, 210096) 2 (Department puter Science and Engineering, Changzhou Institute of Technology, Jiangsu Changzhou, 213006) Abstract Mining on data streams and constructing a classification model has found many applications. The dynamic nature of streaming data poses two key issues facing munities: one is how to monitor the potential changes occurring in data streams, and the other is how to collect sufficient labeled data to adjust the outdated model for adapting to significant changes. In this paper, a semi-supervised learning based algorithm is proposed to attack these problems. We devise a reliable method for monitor and differing significant changes from noisy changes . Whenever the significant changes present , an EM with na? ve Bayes algorithm is employed to augment currently insufficient labeled