Abstract
It is known that different parameters used in Gaussian kernel will provide us different granularities of information granulations. Therefore, kernel based fuzzy rough set has the characteristic of multi-granularity. From this point of view, a multi-granularity attribute reduction strategy is developed in this paper. Different from traditional reduction process that produces reduct by a fixed granularity, our strategy aims to derive reduct which is suitable for fuzzy rough approximations in terms of multi-granularity. To reduce the time consumption in reduction process and to avoid the consideration of all granularities may lead to the difficulty in eliminating attributes, the fuzzy rough approximations derived from the coarsest and the finest granularities are used to design constraint in multi-granularity attribute reduction. The experimental results show that compared with the traditional approach, not only the multi-granularity reduct may bring us almost the same performances for characterizing uncertainties, but also the multi-granularity reduction process is faster since only one reduct is required to be obtained for a set of the fuzzy rough approximations.
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Acknowledgments
This work is supported by the Natural Science Foundation of China (No. 61572242, 61502211, 61503160), Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. 2014002), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_2333).
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Liang, S., Liu, K., Chen, X., Wang, P., Yang, X. (2018). Multi-granularity Attribute Reduction. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_5
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