Browsing 08 Information and Computing Sciences by Author "Qin Yongsong"

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  • Browsing 08 Information and Computing Sciences by Author

Browsing 08 Information and Computing Sciences by Author "Qin Yongsong"

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  • 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 ...
  • Clustering-based Missing Value Imputation for Data Preprocessing 
    Qin Yongsong; Zhu Xiaofeng; Zhang Chengqi; Zhang Jilian; Zhang Shichao (The Institute of Electrical and Electronic Engineers Inc (IEEE), 2006)
    Missing value imputation is an actual yet challenging issue confronted by machine learning and data mining. Existing missing value imputation is a procedure that replaces the missing values in a dataset by some ...
  • Combining Knn Imputation And Bootstrap Calibrated Empirical Likelihood For Incomplete Data Analysis 
    Qin Yongsong; Zhang Shichao; Zhang Chengqi (IGI Publications, 2010)
    The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical ...
  • Difference detection between two contrast sets 
    Huang Huijing; Qin Yongsong; Zhu Xiaofeng; Zhang Ji-Wen; Zhang Shichao (Springer-Verlag, 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 ...
  • Empirical likelihood confidence intervals for differences between two datasets with missing data 
    Qin Yongsong; Zhang Shichao (Elsevier, 2008)
    Detecting differences between populations (or datasets) is an important research topic in machine learning, yet an common application means of evaluating, such as a new medical product by comparing with an old one. Previous ...
  • Estimating confidence intervals for structural differences between contrast groups with missing data 
    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 ...
  • GBKII: An Imputation Method for Missing Values 
    Zhang Chengqi; Zhu Xiaofeng; Zhang Jilian; Qin Yongsong; Zhang Shichao (Springer-Verlag, 2007)
  • Kernel-based multi-imputation for missing data 
    Zhang Shichao; Qin Yongsong; Zhu Xiaofeng; Zhang Jilian; Zhang Chengqi (IOS Press, 2006)
    Missing or incomplete data is a very important problem in many fields of research. Such as in active media technology, opinion polls, market research surveys, mail enquiries, medical studies, and other scientific ...
  • Missing Value Imputation Based on Data Clustering 
    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 ...
  • Optimized parameters for missing data imputation 
    Zhang Shichao; Qin Yongsong; Zhu Xiaofeng; Zhang Jilian; Zhang Chengqi (Springer-Verlag, 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 ...
  • POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases 
    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 ...
  • Semi-parametric optimization for missing data imputation 
    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 ...