Classifying Skewed Data Streams Based on Reusing Data

摘要：

Current research community on data streams mining focuses on mining balanced data streams. However, the skewed class distribution appears in many data streams applications. In this paper, we introduce the method of discovering concept drifting on skewed data streams and propose an algorithm for classifying skewed data streams based on reusing data, RDFCSDS (Reuse Data for Classifying Skewed Data Streams). We evaluate RDFCSDS algorithm on Moving Hyperplane data set. The experiment results show that the sampling method based on reusing data works better than the simple sampling method and cluster sampling method on skewed data streams with concept drifting.

关键词： skewed distribution ensemble classifiers concept drifting

作者: Peng Liu Lijun Cai Yong Wang Longbo Zhang

作者单位: School of Science Northwestern Polytechnical University, China Xian, Shaanxi Province, P.R.China, 7 Department of Mathematics Northwestern Polytechnical University Xian, Shaanxi Province, P.R.China, School of Computer Science and Technology Northwestern Polytechnical University, China Xian, Shaanx College of Computer Science Shandong University of Technology Zibo, Shandong Province, P.R.China, 25

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 90-93

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）