Browsing Closed by Author "Xiao, Yan Shan"

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Browsing Closed by Author "Xiao, Yan Shan"

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  • Cao, Longbing; Luo, Dan; Xiao, Yan Shan; Zheng, Zhigang (Springer Berlin, 2008)
    The collaboration of agents can undertake complicated tasks that cannot be handled well by a single agent. This is even true for excecuting multiple goals at the same time. In this paper, we demonstrate the use of trading ...
  • Liu, Bo; Yin, Jie; Xiao, Yan Shan; Cao, Longbing; Yu, Philip (IEEE, 2010)
    This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value ...
  • Xiao, Yan Shan; Liu, Bo; Cao, Longbing (ACM, 2010)
    Support vector data description (SVDD) is very useful for oneclass classification. However, it incurs high time complexity in handling large scale data. In this paper, we propose a novel and efficient method, named ...
  • Xiao, Yan Shan; Deng, Feiqi; Liu, Bo; Liu, Shouqiang; Luo, Dan; Liang, Guohua (IEEE CS digital library & Springer, 2008)
    The Third International Workshop on Agents and Data Mining Interaction (ADMIa??08) joint with the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. In addition, an EDITED ...
  • Xiao, Yan Shan; Liu, Bo; Luo, Dan; Cao, Longbing (Springer Berlin / Heidelb, 2008)
    Distributed data mining in the CRM is to learn available knowledge from the customer relationship so as to instruct the strategic behavior. In order to resolve the CRM in distributed data mining, this paper proposes the ...
  • Xiao, Yan Shan; Liu, Bo; Cao, Longbing; Wu, Xindong; Zhang, Chengqi; Hao, Zhifeng; Yang, Fengzhao; Cao, Jie (IEEE Computer Society Press, 2009)
    SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a ...
  • Liu, Bo; Xiao, Yan Shan; Cao, Longbing; Yu, Philip (SDM, 2011)
    This paper presents a novel approach to one-class-based uncertain data stream learning. Our proposed approach works in three steps. Firstly, we put forward a local kerneldensity-based method to generate a bound score for ...
  • Liu, Bo; Xiao, Yan Shan; Cao, Longbing; Yu, Philip (ACM, 2010)
    Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on ...
  • Xiao, Yan Shan; Liu, Bo; Cao, Longbing; Yin, Jie; Wu, Xindong (IEEE Computer Society Conference Publishing Services (CPS), 2010)
    Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn useful information from bags of instances. In MIL, the true labels of the instances in positive bags are not always available ...
  • Liu, Bo; Xiao, Yan Shan; Cao, Longbing; Yu, Philip (IEEE Computer Society Conference Publishing Services (CPS), 2010)
    In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works ...