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Consistency approximation: Incremental feature selection based on fuzzy rough set theory
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DOI：10.1016/j.patcog.2024.110652.png)
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Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high -dimensional datasets remain intractable for FRSTbased feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA) -based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CAbased significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high -dimensional datasets.
Keyword:
Fuzzy rough set
Incremental feature selection
Consistency approximation
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
Incremental feature selection by sample selection and feature-based accelerator通过样本选择和基于特征的加速器进行增量特征选择
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