Browsing 08 Information and Computing Sciences by Author "Liu, Bo"

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Browsing 08 Information and Computing Sciences by Author "Liu, Bo"

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  • 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; Deng, Feiqi; Hao, Zhifeng (Inderscience, 2010)
    In this paper, we introduce multiple agents, knowledge discovery and data mining into customer relationship management (CRM) to set up the architecture of a multi-agent-based CRM system (MAB-CRM), and then use the SVMs-based ...
  • 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 ...
  • Liu, Bo; Cao, Longbing; Yu, Philip; Zhang, Chengqi (IEEE, 2008)
    In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable ...
  • 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; Hao, Zhifeng; Tsang, Eric (IEEE-Inst Electrical Electronics Engineers Inc, 2008)
    Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before ...
  • 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 ...
  • Liu, Bo; Xiao, Yanshan; Yu, Philip; Cao, Longbing; Hao, Zhifeng (SIAM, 2013)
    In textual data stream environment, concept drift can occur at any time, existing approaches partitioning streams into chunks can have problem if the chunk boundary does not coincide with the change point which is impossible ...
  • Xiao, Yanshan; Liu, Bo; Yin, Jie; Cao, Longbing; Zhang, Chengqi; Hao, Zhifeng (AAAI Press, 2011)
    Positive and unlabelled learning (PU learning) has been investigated to deal with the situation where only the positive examples and the unlabelled examples are available. Most of the previous works focus on identifying ...
  • 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; Lu, Jie; Zhang, Guangquan; Hao, Zhifeng; Gao, Ya (Atlantis Press, 2007)
  • Liu, Bo; Xiao, Yanshan; Cao, Longbing; Hao, Zhifeng; Deng, Feiqi (Springer, 2013)
    Outlier detection is an important problem that has been studied within diverse research areas and application domains. Most existing methods are based on the assumption that an example can be exactly categorized as either ...
  • 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 ...