Incremental feature selection for dynamic incomplete data using sub-tolerance relations

Jie ZHAO, Yun LING, Faliang HUANG*, Jiahai WANG, Eric W. K. SEE-TO

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Tolerance Rough Set (TRS) theory is commonly employed for feature selection with incomplete data. However, TRS has limitations such as ignoring uncertainty, which often leads to the inclusion of redundant features and diminished classification accuracy. To address these limitations, we propose an extension called Subrelation Tolerance Class (STC). STC decomposes the tolerance relation into two subrelations, enabling a two-stage certainty measurement. This approach progressively filters out certain regions, thereby reducing computational space requirements, and introduces a new significance measure that considers both certain and uncertain information. Leveraging STC and our proposed measure, we develop an incremental feature selection algorithm capable of handling incomplete streaming data. We conduct experiments on real-world datasets and compare the performance with existing algorithms to validate the superiority of our method. The experimental results show that our algorithm reduces the execution time by over 89.78% compared to the baselines while maintaining the classification accuracy.

Original languageEnglish
Article number110125
JournalPattern Recognition
Volume148
Early online date19 Nov 2023
DOIs
Publication statusPublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Incremental feature selection
  • Significance measure
  • Sub-tolerance relation
  • Tolerance rough set

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