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View all- Qi XWu CShi YQi HDuan KWang X(2023)A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention MechanismComputational Intelligence and Neuroscience10.1155/2023/25010222023Online publication date: 1-Jan-2023
Adjustable Fuzzy Rough Reduction: : A Nested Strategy
Authors: 
Ying Shi, Hui Qi, Xiaofang Mu, Mingxing Hou Academic Editor: Daniele BibboAuthors Info & Claims
Abstract
As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.
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Copyright © 2021 Ying Shi et al.
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Published: 01 January 2021
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View all- Qi XWu CShi YQi HDuan KWang X(2023)A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention MechanismComputational Intelligence and Neuroscience10.1155/2023/25010222023Online publication date: 1-Jan-2023
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