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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2023

17.02.2023 | Theoretical Advances

Feature selection using max dynamic relevancy and min redundancy

verfasst von: Kexin Yin, Junren Zhai, Aifeng Xie, Jianqi Zhu

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2023

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Abstract

Feature selection algorithms based on three-way interaction information have been widely studied. However, most of these traditional algorithms only consider class-dependent redundancy, which can lead to an underestimation of redundancy. To address this issue, a feature selection algorithm based on maximum dynamic relevancy minimum redundancy is proposed. The algorithm first proposes a quality coefficient to estimate the feature relevancy. Then we introduce class-independent redundancy to solve the issue of not fully considering redundancy, and propose adaptive coefficients to optimize the algorithm. To ensure the effectiveness of the algorithm, experimental comparisons are carried on 19 benchmark data sets with six algorithms. The results show that the proposed algorithm outperforms other algorithms in terms of performance.
Vorheriger Artikel Feature enhancement modules applied to a feature pyramid network for object detection
Nächster Artikel Feature fusion based on joint sparse representations and wavelets for multiview classification

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Metadaten
Titel
Feature selection using max dynamic relevancy and min redundancy
verfasst von
Kexin Yin
Junren Zhai
Aifeng Xie
Jianqi Zhu
Publikationsdatum
17.02.2023
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-023-01138-y

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