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J. W. Grzymala-Busse and W. J. Grzymala-Busse, “An Experimental Comparison of Three Rough Set Approaches to Missing Attribute Values,” In: J. Peters, A. Skowron, I. Duntsch, J. Grzymala-Busse, E. Orlowska and L. Polkowski, Eds. LNCS Transactions on Rough Sets VI, Springer, Berlin, 2007, pp. 31-50.

has been cited by the following article:

• TITLE: Missing Values Imputation Based on Iterative Learning

  AUTHORS: Huaxiong Li

  KEYWORDS: Incomplete Data; Missing Values Imputation; Iterative Learning; Intension; Extension

  JOURNAL NAME: International Journal of Intelligence Science, Vol.3 No.1A, March 29, 2013

  ABSTRACT: Databases for machine learning and data mining often have missing values. How to develop effective method for missing values imputation is a crucial important problem in the field of machine learning and data mining. In this paper, several methods for dealing with missing values in incomplete data are reviewed, and a new method for missing values imputation based on iterative learning is proposed. The proposed method is based on a basic assumption: There exist cause-effect connections among condition attribute values, and the missing values can be induced from known values. In the process of missing values imputation, a part of missing values are filled in at first and converted to known values, which are used for the next step of missing values imputation. The iterative learning process will go on until an incomplete data is entirely converted to a complete data. The paper also presents an example to illustrate the framework of iterative learning for missing values imputation.