Object feature selection under high-dimension and few-shot data based on three-way decision

Kaifang Wan, Jianmei Wang, Bo Li, Daqing Chen, Linyu Tian

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A feature selection method based on three-way decision is proposed to solve the problem of target recognition under high-dimension and few-shot data. The main research results of this paper include the following aspects: (1) aiming at the uncertainty of existing algorithm caused by uncertain data in high-dimension and few-shot data, based on the traditional filtering feature selection ReliefF algorithm, the algorithm is improved, according to the three-way decision thought, the single threshold of original algorithm is extended into two thresholds, to increase the rate of fault tolerance of the original algorithm, avoid features is too much or too little to eliminate. (2) To solve the problem of "dimension disaster" of high-dimension and few-shot data, the features are divided into three domains according to two thresholds and feature weights, and then the features in the boundary domain are further selected by combining the wrapper feature selection idea to reduce the dimension and obtain the optimal feature subset. Finally, the experimental results show that the recognition accuracy and F1 have been improved. Experiment 1 proved the fault tolerance rate and stability of the proposed algorithm, and Experiment 2 verified the advantages of the proposed algorithm in small sample conditions.

Original languageEnglish
Pages (from-to)2261-2275
Number of pages15
JournalVisual Computer
Volume39
Issue number6
DOIs
StatePublished - Jun 2023

Keywords

  • Feature extraction
  • Feature selection
  • Few shot
  • Three-way decision

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