基于贝叶斯的多窗口数据流分类研究

提出一种基于贝叶斯的多窗口数据流分类模型BCCDSMW对数据流进行分类。BC-CDSMW对时间窗口内的数据进行压缩。只有少量样本被保存,其他样本只保存少量统计量,以便在有限的空间上尽可能多地利用有效历史数据。目的是在适应概念漂移的前提下,利用多个时间段的数据学习生成单个贝叶斯分类器,使其能准确地反映当前数据流地实际情况,并且该分类器能快速地对未来数据分类处理。

Research on Multi-Windows Data Streams Classifying Based on Bayesian Classifier

Proposes a Bayesian classifier for classifying data streams based on Multi-Windows（BCCDSMW） to classify data streams.BCCDSMW compresses the chunks of different time windows in the data stream.We only preserve few samples and preserve simple statistics for other samples,to make use of history data effectively in the limited space.In the adaptation of concept drifting,BCCDSMW constructs a single Bayesian Classifier from different chunks,which can reflect the current situation of the data streams and classify the coming testing examples quickly.