The research of attribute reduction algorithm based on extension neighborhood relation

被引：0
作者：
Department of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China [1 ]
机构：
[1] Department of Information Engineering, Taiyuan University of Technology
来源：
J. Comput. Inf. Syst. | / 16卷 / 6613-6620期
关键词：
Attribute reduction; Decision system; Extension neighborhood relation; Neighborhood threshold; Variable precision threshold;
D O I：
10.12733/jcisP0765
中图分类号：
学科分类号：
摘要：
By introducing a novel attribute reduction algorithm based on an extension neighborhood relation, it defeated the decision problem of the complete mixed system in classical rough sets theory. The proposed algorithm could also treat the incomplete mixed decision system, which missed some attribute values in complete mixed attribute data sets. The neighborhood threshold and the variable precision threshold were employed in the extension neighborhood relation as the restrictions to select positive region of decision, and the significance of attributes in this positive region was taken as the heuristic factor. The outstanding property of the proposed algorithm was to handle the nominal attributes, the numerical attributes and the missing attributes simultaneously without discretizing the numerical attributes or completing the incomplete data. The proposed algorithm was tested on the UCI data sets, and the experiment results showed that it could select the core attributes still keeping or even improving classification accuracy. Also it was discussed that how to influence the classification when specifying the values of the two mentioned threshold parameters used in the extension neighborhood relation. © 2013 Binary Information Press.
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页码：6613 / 6620
页数：7
相关论文
共 13 条
  • [1] Pawlak Z., Rough SetsTheoretical Aspects of Reasoning about Data, (1991)
  • [2] Wang G.Y., Extension of rough set under incomplete information systems, Journal of Computer Research and Development, 39, 10, pp. 1238-1243, (2002)
  • [3] Lin T.Y., Neighborhood systems and relational database, Proc. CSC'88, pp. 725-740, (1988)
  • [4] Lin T.Y., Neighborhood systems and approximation in database and knowledge base systems, Proc. The Fourth International Symposium on Methodologies of Intelligent Systems'89, pp. 75-86, (1989)
  • [5] Lin T.Y., Neighborhood systems: Mathematical models of information granulations, Proc. IEEE International Conference on Systems'03, pp. 3188-3193, (2003)
  • [6] Lin T.Y., Granular computing: Practices, theories and future directions, Encyclopedia of Complexity and Systems Science, 770, 9, pp. 4339-4355, (2008)
  • [7] Hu Q.H., Yu D.R., Xie Z.X., Numerical attribute reduction based on neighborhood granulation and rough approximation, Journal of Software, 19, pp. 640-649, (2008)
  • [8] Hu Q.H., Yu D.R., Xie Z.X., Neighborhood classifiers, Expert Systems with Applications, 34, 2, pp. 866-876, (2008)
  • [9] Li N., The research of attribute reduction algorithm based on neighborhood rough set, (2011)
  • [10] Huo Z.C., Zeng L., Fan T., Et al., New data analysis method in incomplete information system with hybrid value, Application Research of Computers, 28, 9, pp. 3321-3323, (2011)
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