Browsing 08 Information and Computing Sciences by Title

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Browsing 08 Information and Computing Sciences by Title

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  • Liu, T; Lipnicki, DM; Zhu, W; Tao, D; Zhang, C; Cui, Y; Jin, JS; Sachdev, PS; Wen, W (2012-02-21)
    Alzheimer's disease (AD) is characterized by an insidious onset of progressive cerebral atrophy and cognitive decline. Previous research suggests that cortical folding and sulcal width are associated with cognitive function ...
  • Lu, Jie; Zhang, Guangquan (NA, 2002)
  • Lu, J; Bai, C; Zhang, G (Pergamon-Elsevier Science Ltd, 2009-01)
    This study applies Bayesian network techniques to analyze and verify the relationships among cost factors and benefit factors in e-service systems. This study first establishes a Bayesian network for e-service cost-benefit ...
  • Zhu, X; Wu, X (IEEE Computer Soc, 2005-01)
    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 ...
  • Qin, Z; Zhang, C; Wang, T; Zhang, S (2010)
    Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. ...
  • Wang, T; Qin, Z; Zhang, S; Zhang, C (2012-07)
    It is an actual and challenging issue to learn cost-sensitive models from those datasets that are with few labeled data and plentiful unlabeled data, because some time labeled data are very difficult, time consuming and/or ...
  • Qin, Z; Wang, T; Zhang, C; Zhang, S (Springer, 2013-01)
    Cost-sensitive learning algorithms are typically motivated by imbalance data in clinical diagnosis that contains skewed class distribution. While other popular classification methods have been improved against imbalance ...
  • Qin, Z; Zhang, S; Zhang, C (2004)
    How to minimize misclassification errors has been the main focus of Inductive learning techniques, such as CART and C4.5. However, misclassification error is not the only error in classification problem. Recently, researchers ...
  • Zhu, X; Zhang, S; Zhang, J; Zhang, C (AAAI Press, 2007-01)
    Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with optimal ordering. Experimental results ...
  • Qin, Z; Zhang, S; Liu, L; Wang, T (IEEE Computer Society, 2008-01)
    In many real world data mining and classification tasks, we face with the problem of high cost in making training data sets. In addition, in many domains, different misclassification errors involve different costs. These ...
  • Zhang, S; Zhu, X; Zhang, J; Zhang, C (2007)
    Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassiflcation cost and test cost at present. In real world ...
  • Zhang, W; Zhang, Y; Cheema, MA; Lin, X (ACM, 2010-01)
    Aggregation against distinct objects has been involved in many real applications with the presence of duplicates, including real-time monitoring moving objects. In this paper, we investigate the problem of counting distinct ...
  • Qiao, Y; Tartary, C (Springer, 2008-01)
    In the Crypto'07 paper [5], Desmedt et al. studied the problem of achieving secure n-party computation over non-Abelian groups. The function to be computed is f G (x 1,...,x n ) :?=?x 1 ·...·x n where each participant P i ...
  • Wang, C; She, Z; Cao, L (IJCAI/AAAI, 2013-01)
    The usual representation of quantitative data is to formalize it as an information table, which assumes the independence of attributes. In real-world data, attributes are more or less interacted and coupled via explicit ...
  • Song, Y; Cao, L; Wu, X; Wei, G; Ye, W; Ding, W (2012)
    In stock markets, an emerging challenge for surveillance is that a group of hidden manipulators collaborate with each other to manipulate the price movement of securities. Recently, the coupled hidden Markov model (CHMM)-based ...
  • Cao, L; Ou, Y; Yu, P (IEEE Computer Soc, 2012-01)
    Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based ...
  • Wang, C; She, Z; Cao, L (IEEE, 2013-01)
    Clustering ensemble is a powerful approach for improving the accuracy and stability of individual (base) clustering algorithms. Most of the existing clustering ensemble methods obtain the final solutions by assuming that ...
  • Cao, W; Cao, L; Song, Y (IEEE, 2013-01)
    Financial crisis detection is a long-standing challenging issue with significant practical values and impact on economy, society and globalization. The challenge lies in many aspects, in particular, the nonlinear and dynamic ...
  • Cheng, X; Miao, D; Wang, C; Cao, L (IEEE, 2013-01)
    Traditional document clustering approaches are usually based on the Bag of Words model, which is limited by its assumption of the independence among terms. Recent strategies have been proposed to capture the relation between ...
  • Zhao, L; Hoi, SCH; Li, Z; Wong, L; Nguyen, H; Li, J (2014)