基于概念漂移检测的网络数据流分类

互联网环境日新月异,使得网络数据流中存在概念漂移,对数据流的分类也由传统的静态分类变为动态分类,而如何对概念漂移进行检测是动态分类的关键.本文提出一种基于概念漂移检测的网络数据流自适应分类算法,通过比较滑动窗口中数据与历史数据的分布差异来检测概念漂移,然后将窗口中数据过采样来减少样本间的不均衡性,最后将处理后的数据集输...

Network Data Stream Classification Based on Concept Drift Detection

With the rapid development of the Internet environment, the concept drift may exist in the network data stream. The classification of the data stream has changed from the traditional static classification to the dynamic classification. The key of dynamic classification is how to detect the concept drift. In this paper, an adaptive classification algorithm for network data streams based on concept drift detection is proposed. The algorithm detects concept drift by comparing the differences of distribution difference between the data in the sliding window and historical data, and then the window data is oversampled to reduce the imbalance between the samples, finally, the processed data sets are input into OS-ELM classifier for online learning, it updates the classifier to cope with the concept drift in the data stream. In this paper, the proposed algorithm is tested on the MOA experimental platform by using synthetic data sets and real data sets. The results show that the classification accuracy and stability of the algorithm are improved compared with the traditional ensemble learning algorithm, and with the increase of data flow, the advantage of time performance begins to show, which is suitable for complex and changeable network environment.