A Learning Algorithm for One-class Data Stream Classification Based on Ensemble Classifier
Current research on data stream classification mainly focuses on supervised learning, in which a fully labeled data stream is needed for training. However, fully labeled data streams are expensive to obtain, which makes the supervised learning approach difficult to be applied to real-life applications. In this paper, we consider the problem of one-class classification on data stream with respect to concept drift where a large volume of data arrives at a high speed and with change of concept. In this case, only a small number of positively labeled examples is available for training. We propose our OcEC(One-class Ensemble Classifiers)algorithm and have it compared with WEC1 algorithm and SEA2 algorithm. Experimental study on both Moving Hyperplane dataset and SEA dataset shows that the OcEC algorithm has excellent classification performance and can quickly adapt to concept drift
one-class classification data stream ensemble classifier (key words)
Dong Zhang Lijun Cai Yong Wang Longbo Zhang
Department of Mathematics Northwestern Polytechnical University Xian, Shaanxi Province, P.R.China, Department of Computer Northwestern Polytechnical University Xian, Shaanxi Province, P.R.China, 710 College of Computer Science Shandong University of Technology Zibo, Shandong Province, P.R.China, 25
国际会议
太原
英文
596-600
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）