Consistency approximation: Incremental feature selection based on fuzzy rough set theory

Jie ZHAO, Daiyang WU, Jiaxin WU, Wenhao YE, Faliang HUANG*, Jiahai WANG, Eric W. K. SEE-TO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


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 FRST-based 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 CA-based 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.
Original languageEnglish
Article number110652
JournalPattern Recognition
Early online date5 Jun 2024
Publication statusE-pub ahead of print - 5 Jun 2024

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  • Consistency approximation
  • Fuzzy rough set
  • Incremental feature selection


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