Improving the Performance of Data Stream Classifiers by Mining Recurring Contexts

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• 作者：Yong Wang ; Zhanhuai Li ; Yang Zhang ; Longbo Zhang ; Yun Jiang
• 刊名：Lecture Notes in Computer Science
• 出版年：2006
• 出版时间：2006
• 年：2006
• 卷：4093
• 期：1
• 页码：1094-1106
• 全文大小：294 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Traditional researches on data stream mining only put emphasis on building classifiers with high accuracy, which always results in classifiers with dramatic drop of accuracy when concept drifts. In this paper, we present our RTRC system that has good classification accuracy when concept drifts and enough samples are scanned in data stream. By using Markov chain and least-square method, the system is able to predict not only on which the next concept is but also on when the concept is to drift. Experimental results confirm the advantages of our system over Weighted Bagging and CVFDT, two representative systems in streaming data mining.