Minimal Element Selection in the Discernibility Matrix for Attribute Reduction

JIANG Yu

doi:10.1049/cje.2018.06.018

Volume 28 Issue 1

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JIANG Yu. “Minimal Element Selection in the Discernibility Matrix for Attribute Reduction”. Chinese Journal of Electronics, vol. 28 no. 1. doi: 10.1049/cje.2018.06.018

Citation:

JIANG Yu. “Minimal Element Selection in the Discernibility Matrix for Attribute Reduction”. Chinese Journal of Electronics, vol. 28 no. 1. doi: 10.1049/cje.2018.06.018

JIANG Yu. “Minimal Element Selection in the Discernibility Matrix for Attribute Reduction”. Chinese Journal of Electronics, vol. 28 no. 1. doi: 10.1049/cje.2018.06.018

Citation:

JIANG Yu. “Minimal Element Selection in the Discernibility Matrix for Attribute Reduction”. Chinese Journal of Electronics, vol. 28 no. 1. doi: 10.1049/cje.2018.06.018

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Minimal Element Selection in the Discernibility Matrix for Attribute Reduction

doi: 10.1049/cje.2018.06.018

JIANG Yu

College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China

Funds: This work is supported by the Young and Middle-aged Academic Leader Foundation of Chengdu University of Information Technology (No.J201609).

Received Date: 2016-07-08
Rev Recd Date: 2016-12-01
Publish Date: 2019-01-10

Abstract

Abstract

Discernibility matrix is a beautiful theoretical result to get reducts in the rough set, but the existing algorithms based on discernibility matrix share the same problem of heavy computing load and large store space, since there are numerous redundancy elements in discernibility matrix and these algorithms employ all elements to find reducts. We introduce a new method to compute attribute significance. A novel approach is proposed, called minimal element selection tree, which utilizes many strategies to eliminate redundancy elements in discernibility matrix. This paper presents two methods to find out a minimal reduct for a given decision table based this tree structure. The experimental results with UCI data show that the proposed approaches are effective and efficient than the benchmark methods.
- Rough set,
- Discernibility matrix,
- Minimal element selection tree,
- Attribute significance,
- Minimal reduct

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References(24)

References

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