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Discernibility matrix simplification with new attribute dependency functions for incomplete information systems

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Abstract

Recently, many researches have been done on attribute dependency degree models. In this work, we bring forward three attribute dependency functions for incomplete information systems and investigate their basic properties in detail. Afterward, we apply the proposed models to twelve data sets from the UCI repository of machine learning databases. Finally, using the proposed functions, we perform the discernibility matrix simplification of incomplete information systems. The experimental results show that our proposed functions are more flexible to calculate the degree of each conditional attribute related to the decision attribute for incomplete information systems.

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Acknowledgments

We would like to thank the anonymous reviewers very much for their professional comments and valuable suggestions. This work is supported by the National Natural Science Foundation of China (NO. 11071061) and the National Basic Research Program of China (NO. 2010CB334706, 2011CB311808).

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Authors and Affiliations

  1. College of Mathematics and Econometrics, Hunan University, Changsha, 410082, Hunan, People’s Republic of China

    Guangming Lang & Qingguo Li

  2. College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, People’s Republic of China

    Lankun Guo

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  1. Guangming Lang
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  2. Qingguo Li
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Correspondence to Qingguo Li.

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Lang, G., Li, Q. & Guo, L. Discernibility matrix simplification with new attribute dependency functions for incomplete information systems. Knowl Inf Syst 37, 611–638 (2013). https://doi.org/10.1007/s10115-012-0589-3

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