作　　者： (王军）; (刘三民）; (刘涛）;

机构地区： 安徽工程大学计算机与信息学院,安徽芜湖241000

出　　处： 《内江师范学院学报》 2017年第8期51-55,72,共6页

摘　　要： 提出一种基于分类器相似性加权和差异性集成的数据流分类方法.用最新基分类器作为参照分类器,代表数据流中即将出现的概念,基于此分类器通过Gower相似系数求出基分类器之间的相似性,并以相似性作为基分类器权值进行加权多数投票;同时采用Q-statistic方法计算出参照分类器与其他基分类器之间的差异性,并根据差异性大小淘汰较弱基分类器保持集成分类模型多样性.最终构建的集成模型在标准仿真数据集上进行实验仿真.结果表明:在对隐含噪声的动态数据流进行分类时,该方法分类准确率比传统集成分类方法约提高11%,具有良好的分类准确率和抗噪稳定性. A new method of data stream classification based on similarity weighting and differential integration of classifiers is proposed.The method uses the latest base classifier as the reference classifier,representing the upcoming concept in the data stream.Based on this classifier,the similarity between the base classifiers is worked out by use of the Gower’s similarity coefficient,and the similarity is used as the base classifier weights to conduct weighted majority vote.At the same time,Q-statistic method is adopted to calculate the difference between referenced classifiers and other base classifiers,and according to the size of the difference,the relatively weak base classifiers were eliminated so that the diversity of the integrated classification model can be kept.Lastly,simulation experiment is carried out on standard simulation dataset,and the results show that the classification accuracy of the presented method is about 11%higher than that of the traditional integrated classification method when used to classify dynamic data flow with noise,indicating the method is of good classification accuracy and anti-noise stability.

关 键 词： 噪声 动态数据流 最新基分类器 集成分类