Browsing by Author "Wu Xindong"

UTSePress Research/Manakin Repository

Search UTSePress Research

Browse

My Account

Browsing by Author "Wu Xindong"

Sort by: Order: Results:

  • An Agent-Based Hybrid System for Microarray Data Analysis 
    Zhang Zili; Yang P; Wu Xindong; Zhang Chengqi (Ieee Computer Soc, 2009)
    This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in ...
  • Approximate Repeating Pattern Mining with Gap Requirements 
    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 ...
  • Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval 
    Tao Dacheng; Tang Xiaoou; Li Xuelong; Wu Xindong (IEEE Computer Soc, 2006)
    Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of ...
  • Bayesian Tensor Approach for 3-D Face Modeling 
    Tao Dacheng; Song Mingli; Li Xuelong; Shen Jialie; Sun Jimeng; Wu Xindong; Faloutsos Christos; Maybank Stephen (IEEE-Inst Electrical Electronics Engineers Inc, 2008)
    Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D ...
  • Bridging Local And Global Data Cleansing: Identifying Class Noise In Large, Distributed Data Datasets 
    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 ...
  • Ci Builder: A system prototype for building confidence intervals for differnces of groups 
    Wu Xindong; Qin Yongsong; Zhang Shichao; Zhang Jilian; Zhu Xiaofeng (Association for Computing Machinery, 2006)
    Mining the differences between contrasting groups is an important and challenging task in real world applications such as medical research, social network analysis and link discovery. Yet another important issue that ...
  • CLAP: Collaborative pattern mining for distributed information systems 
    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 ...
  • Class Noise Handling For Effective Cost-Sensitive Learning By Cost-Guided Iterative Classification Filtering 
    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 ...
  • Cleansing Noisy Data Streams 
    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 ...
  • Computing the minimum-support for mining frequent patterns 
    Zhang Shichao; Wu Xindong; Zhang Chengqi; Lu Jingli (Springer, 2008)
    Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder ...
  • Conceptual equivalence for contrast mining in classification learning 
    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 ...
  • Corrective Classification: Learning From Data Imperfections With Aggressive And Diverse Classifier Ensembling 
    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 ...
  • Cost-Constrained Data Acquisition For Intelligent Data Preparation 
    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 ...
  • Cross-Domain Collaborative Filtering over Time. 
    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 ...
  • Database classification for multi-database mining 
    Wu Xindong; Zhang Chengqi; Zhang Shichao (Elsevier Science, 2003)
    Many large organizations have multiple databases distributed in different branches, and therefore multi-database mining is an important task for data mining. To reduce the search cost in the data from all databases, we ...
  • A decremental algorithm for maintaining frequent itemsets in dynamic databases 
    Zhang Shichao; Wu Xindong; Zhang Ji-Wen; Zhang Chengqi (Springer-Verlag, 2001)
  • Effective Classification Of Noisy Data Streams With Attribute-Oriented Dynamic Classifier Selection 
    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 ...
  • Efficient mining of both positive and negative Association rules 
    Wu Xindong; Zhang Chengqi; Zhang Shichao (Australasian Medical Publishing Company, 2003)
  • Efficient String Matching With Wildcards And Length Constraints 
    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. ...
  • Ensemble Pruning via Individual Contribution Ordering 
    Lu Zhenyu; Wu Xindong; Zhu Xingquan; Bongard Josh (ACM, 2010)
    An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend to construct unnecessarily large ensembles, ...