Browsing 08 Information and Computing Sciences by Author "Zhu, Xingquan"

UTSePress Research/Manakin Repository

Search UTSePress Research


Advanced Search

Browse

My Account

  • >
  • >

Browsing 08 Information and Computing Sciences by Author "Zhu, Xingquan"

Sort by: Order: Results:

  • Fang, Meng; Yin, Jie; Zhu, Xingquan (ACM, 2013)
    Modern information networks, such as social networks, are often characterized with large sizes and dynamic changing structures. To analyze these networks, existing solutions commonly rely on graph sampling techniques to ...
  • Zhu, Xingquan; Fu, Yifan; Elmagarmid, Ahmed (IEEE, 2013)
    Active learning (AL) traditionally relies on some instance-based utility measures (such as uncertainty) to assess individual instances and label the ones with the maximum values for training. In this paper, we argue that ...
  • He, Dan; Zhu, Xingquan; Wu, Xindong (IEEE Computer Society, 2009)
    In this paper, we define a new research problem for mining approximate repeating patterns (ARP) with gap constraints, where the appearance of a pattern is subject to an approximate matching, which is very common in biological ...
  • Wu, Jia; Zhu, Xingquan; Zheng, Sanyou; Cai, Zhihua (IEEE, 2013)
    Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this ...
  • Zhu, Xingquan; Wu, Xindong; Chen, Qijun (Springer, 2006)
    To cleanse mislabeled examples from a training dataset for efficient and effective induction, most existing approaches adopt a major set oriented scheme: the training dataset is separated into two parts (a major set and a ...
  • Pan, Shirui; Zhu, Xingquan (ACM, 2012)
    In this paper, we propose to query correlated graph in a data stream scenario, where given a query graph q an algorithm is required to retrieve all the subgraphs whose Pearson?s correlation coe?cients with q are greater ...
  • Zhu, Xingquan; Li, Bin; Wu, Xindong; He, Dan; Zhang, Chengqi (Elsevier Science Bv, 2011)
    The purpose of data mining from distributed information systems is usually threefold: (1) identifying locally significant patterns in individual databases; (2) discovering emerging significant patterns after unifying ...
  • Zhu, Xingquan; Wu, Xindong (IEEE Computer Soc, 2006)
    Recent research in machine learning, data mining, and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the ...
  • 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 ...
  • Yang, Y; Wu, Xindong; Zhu, Xingquan (Elsevier Science Bv, 2008)
    Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes ...
  • Zhang, Y; Zhu, Xingquan; Wu, Xindong; Bond, Jeffrey (Pergamon-Elsevier Science Ltd, 2011)
    Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or ...
  • Zhu, Xingquan; Wu, Xindong (IEEE Computer Soc, 2005)
    Real-world data is noisy and can often suffer from corruptions or incomplete values that may impact the models created from the data. To build accurate predictive models, data acquisition is usually adopted to prepare the ...
  • Li, Bin; Zhu, Xingquan; Li, Ruijiang; Zhang, Chengqi; Xue, Xiangyang; Wu, Xindong (AAAI Press, 2011)
    Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture ...
  • Zhu, Xingquan (IEEE-inst Electrical Electronics Engineers Inc, 2011)
    Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples In this paper, sample and instance are interchangeable terms. by including them into the training set through an automated labeling process. Such ...
  • Sun, D.H.; Liu, Li; Zhang, P.; Zhu, Xingquan; Shi, Y. (Idea Publishing Group, 2011)
    Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated ...
  • Fu, Yifan; Li, Bin; Zhu, Xingquan; Zhang, Chengqi (ACM, 2011)
    Traditional active learning methods request experts to provide ground truths to the queried instances, which can be expensive in practice. An alternative solution is to ask nonexpert labelers to do such labeling work, which ...
  • Zhu, Xingquan; Wu, Xindong; Yang, Y (Springer London Ltd, 2006)
    Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data ...
  • Chen, G; Wu, Xindong; Zhu, Xingquan; Arslan, An; He, Y (Springer London Ltd, 2006)
    This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. ...
  • Liang, Guohua; Zhu, Xingquan; Zhang, Chengqi (AAAI Press, 2011)
    Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with ...
  • Liang, Guohua; Zhu, Xingquan; Zhang, Chengqi (Springer-Verlag Berlin / Heidelberg, 2011)
    Research into learning from imbalanced data has increasingly captured the attention of both academia and industry, especially when the class distribution is highly skewed. This paper compares the Area Under the Receiver ...