期刊
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
卷 26, 期 4, 页码 521-551出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218488518500253
关键词
Rough set; positive region; relative discernibility; core attribute; attribute reduction
资金
- National Science Foundation of China [61402005]
- Anhui Provincial Natural Science Foundation [1508085MF126]
- Excellent Young Foundation of Chuzhou University [2013RC003]
- Key Laboratory of Computation Intelligence and Signal Processing of Education Ministry Foundation
- Initial Scientific Research of Chuzhou University [2016qd07, KJZ03]
Attribute reduction is one of key issues in rough set theory, and positive region reduct is a classical type of reducts. However, a lot of reduction algorithms have more high time expenses when dealing with high-volume and high-dimensional data sets. To overcome this shortcoming, in this paper, a relative discernibility reduction method based on the simplified decision table of the original decision table is researched for obtaining positive region reduct. Moreover, to further improve performance of reduction algorithm, we develop an accelerator for attribute reduction, which reduces the radix sort times of the reduction process to raise algorithm efficiency. By the accelerator, two positive region reduction algorithms, i.e., FARA-RS and BARA-RS, based on the relative discernibility are designed. FARA-RS simultaneously reduce the size of the universe and the number of radix sort to achieve speedup and BARA-RS only reduce the number of radix sort to achieve acceleration. The experimental results show that the proposed reduction algorithms are effective and feasible for high dimensional and large data sets.
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