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Incremental approaches to knowledge reduction based on characteristic matrices

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Abstract

Knowledge reduction is complicated with the dynamic change of the object set in applications. In this paper, we propose incremental approaches to computing the type-1 and type-2 characteristic matrices of coverings with respect to variation of objects. Also we present two incremental algorithms of calculating the second and sixth lower and upper approximations of sets when adding and deleting more objects in dynamic covering approximation spaces. Subsequently, we employ experiments to validate that the incremental approaches are more effective and efficient to construct approximations of sets in dynamic covering information systems. Finally, we preform knowledge reduction of dynamic covering decision information systems by using the incremental approaches.

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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 (Nos. 11371130, 11401052, 11401195, 11201137, 11201490), the Scientific Research Fund of Hunan Provincial Education Department (No. 14C0049).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Changsha University of Science and Technology, Changsha, Hunan, 410114, China

    Guangming Lang & Qimei Xiao

  2. College of Mathematics and Econometrics, Hunan University, Changsha, Hunan, 410082, China

    Qingguo Li & Mingjie Cai

  3. College of Science, Central South University of Forestry and Technology, Changsha, Hunan, 410082, China

    Tian Yang

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  1. Guangming Lang
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Correspondence to Guangming Lang.

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Lang, G., Li, Q., Cai, M. et al. Incremental approaches to knowledge reduction based on characteristic matrices. Int. J. Mach. Learn. & Cyber. 8, 203–222 (2017). https://doi.org/10.1007/s13042-014-0315-4

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