Browsing by Author "Zhang, Peng"

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Browsing by Author "Zhang, Peng"

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  • Zhu, Xingquan; Lin, Xuemin; Shi, Yuquan; Zhang, Peng (IEEE-Inst Electrical Electronics Engineers Inc, 2010)
    In this paper, we propose a new research problem on active learning from data streams, where data volumes grow continuously, and labeling all data is considered expensive and impractical. The objective is to label a small ...
  • Zhu, Xingquan; Zhang, Peng; Wu, Xindong; He, Dan; Zhang, Chengqi; Shi, Yong (IEEE Computer Society, 2008)
    We identify a new research problem on cleansing noisy data streams which contain incorrectly labeled training examples. The objective is to accurately identify and remove mislabeled data, such that the prediction models ...
  • Zhang, Peng; Gao, Byron; Zhu, Xingquan; Guo, Li (IEEE, 2011)
    Lazy learning, such as k-nearest neighbor learning, has been widely applied to many applications. Known for well capturing data locality, lazy learning can be advantageous for highly dynamic and complex learning environments ...
  • Zhang, Peng; Zhu, Xingquan; Guo, Li (IEEE Computer Society, 2009)
    In this paper, we propose a framework to build prediction models from data streams which contain both labeled and unlabeled examples. We argue that due to the increasing data collection ability but limited resources for ...
  • Zhang, Peng; Zhu, Xingquan; Zhang, Zhiwang; Shi, Yong (IOS Press, 2010)
    Excessive lose of customer account is becoming a major headache for VIP E-Mail hosting companies. Analysis of what kind of customer is more prone to lose and finding the appropriate measures to sustain those customers has ...
  • Zhang, Peng; Zhu, Xingquan; Shi, Yong; Guo, Li; Wu, Xindong (Elsevier Science Bv, 2011)
    In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction ...
  • Zhang, Peng; Zhu, Xingquan; Tan, Jianlong; Guo, Li (ACM, 2010)
    Missing data commonly occur in many applications. While many data imputation methods exist to handle the missing data problem for databases, when applied to concept drifting data streams, these methods share some common ...