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

UTSePress Research/Manakin Repository

Search UTSePress Research


Advanced Search

Browse

My Account

  • >
  • >

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

Sort by: Order: Results:

  • Ni, Ailing; Zhu, Xiaofeng; Zhang, Chengqi (Springer, 2005)
    Fully taking into account the hints possibly hidden in the absent data, this paper proposes a new criterion when selecting attributes for splitting to build a decision tree for a given dataset. In our approach, it must pay ...
  • Zhang, Shichao; Wu, Xindong; Qin, Yongsong; Zhang, Jilian; Zhu, Xiaofeng (ACM Press, 2006)
  • Zhang, Chengqi; Qin, Yongsong; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Shichao (IEEE, 2006)
  • Zhu, Xiaofeng; Zhang, Shichao; Zhang, Jilian; Zhang, Chengqi (AAAI Press, 2007)
    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 ...
  • Zhang, Shichao; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (Springer, 2007)
    Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassification cost and test cost at present. In real world ...
  • Huang, Huijing; Qin, Yongsong; Zhu, Xiaofeng; Zhang, Ji-Wen; Zhang, Shichao (Springer-Verlag Berlin, 2006)
    Mining group differences is useful in many applications, such as medical research, social network analysis and link discovery. The differences between groups can be measured from either statistical or data mining perspective. ...
  • Qin, Yongsong; Zhang, Shichao; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (Elsevier Science, 2009)
    Difference detection is actual and extremely useful for evaluating a new medicine B against a specified disease by comparing to an old medicine A, which has been used to treat the disease for many years. The datasets ...
  • Zhang, Chengqi; Zhu, Xiaofeng; Zhang, Jilian; Qin, Yongsong; Zhang, Shichao (Springer, 2007)
    Missing data imputation is an actual and challenging issue in machine learning and data mining. This is because missing values in a dataset can generate bias that affects the quality of the learned patterns or the ...
  • Zhang, Shichao; Qin, Yongsong; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (IOS Press, 2006)
  • Zhang, Jilian; Zhang, Shichao; Zhu, Xiaofeng; Wu, Xindong; Zhang, Chengqi (AAAI Press, 2007)
    In this paper, we propose an empirical likelihood (EL) based strategy for building confidence intervals for differences between two contrasting groups. The proposed method can deal with the situations when we know little ...
  • Zhang, Shichao; Huang, Zifang; Zhang, Jilian; Zhu, Xiaofeng (Springer, 2008)
    Research on traditional association rules has gained a great attention during the past decade. Generally, an association rule A a?? B is used to predict that B likely occurs when A occurs. This is a kind of strong correlation, ...
  • Zhang, Jilian; Zhu, Xiaofeng; Li, Xianxian; Zhang, Shichao (Springer, 2013)
    Recommender systems can predict individual user?s preference (individual rating) on items by examining similar items? popularity or similar users? taste. However, these systems cannot tell item?s long-term popularity. ...
  • Zhang, Shichao; Jin, Zhi; Zhu, Xiaofeng; Zhang, Jilian (Springer, 2009)
    Many missing data analysis techniques are of single-imputation. However, single-imputation cannot provide valid standard errors and confidence intervals, since it ignores the uncertainty implicit in the fact that the imputed ...
  • Zhang, Shichao; Zhang, Jilian; Zhu, Xiaofeng; Qin, Yongsong; Zhang, Chengqi (Springer, 2008)
    We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we ...
  • Zhu, Xiaofeng; Zhang, Jilian; Zhang, Shichao (Springer, 2013)
    This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) ...
  • Zhang, Shichao; Jin, Zhi; Zhu, Xiaofeng (Springer-Verlag, 2008)
    Many missing data imputation methods are based on only complete instances (instances without missing values in a dataset) when estimating plausible values for the missing values in the dataset. Actually, the information ...
  • Zhang, Shichao; Qin, Yongsong; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (Springer, 2006)
    To complete missing values, a solution is to use attribute correlations within data. However, it is difficult to identify such relations within data containing missing values. Accordingly, we develop a kernel-based missing ...
  • Qin, Yongsong; Zhang, Shichao; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (Elsevier Science, 2009)
    To complete missing values a solution is to use correlations between the attributes of the data. The problem is that it is difficult to identify relations within data containing missing values. Accordingly, we develop a ...
  • Qin, Yongsong; Zhang, Shichao; Zhu, Xiaofeng; Zhang, Jilian; Zhang, Chengqi (Springer, 2007)
    Missing data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. Our imputation method ...
  • Zhang, Shichao; Liu, Li; Zhu, Xiaofeng; Shan, Chen (IEEE Computer Society, 2008)
    Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node ...