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Pelckmans, K., et al., Handling missing values in support vector machine classifiers. Neural Networks, 2005. 18(5-6): p. 684-692.

has been cited by the following article:

• TITLE: Quantization of Rough Set Based Attribute Reduction

  AUTHORS: Bing Li, Peng Tang, Tommy W. S. Chow

  KEYWORDS: attribute reduction; extension rule; distribution of solutions; quantization

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 25, 2013

  ABSTRACT: We demonstrate rough set based attribute reduction is a sub-problem of propositional satisfiability problem. Since satisfiability problem is classical and sophisticated, it is a smart idea to find solutions of attribute reduction by methods of satisfiability. By extension rule, a method of satisfiability, the distribution of solutions with different numbers of attributes is obtained without finding all attribute reduction. The relation between attribute reduction and missing is also analyzed from computational cost and amount of solutions.