Pseudo-Multiview Learning Using Subjective Logic for Enhanced Classification Accuracy

Dat Ngo ()
Additional contact information
Dat Ngo: Department of Computer Engineering, Korea National University of Transportation, Chungju 27469, Republic of Korea

Mathematics, 2025, vol. 13, issue 13, 1-16

Abstract: Deep learning has significantly advanced image classification by leveraging hierarchical feature representations. A key factor in enhancing classification accuracy is feature concatenation, which integrates diverse feature sets to provide a richer representation of input data. However, this fusion strategy has inherent limitations, including increased computational complexity, susceptibility to redundant or irrelevant features, and challenges in optimally weighting different feature contributions. To address these challenges, this paper presents a pseudo-multiview learning method that dynamically combines different views at the evidence level using a belief-based model known as subjective logic. This approach adaptively assigns confidence levels to each view, ensuring more effective integration of complementary information while mitigating the impact of noisy or less relevant features. Experimental evaluations of datasets from various domains demonstrate that the proposed method enhances classification accuracy and robustness compared with conventional classification techniques.

Keywords: image classification; information fusion; subjective logic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/13/2085/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/13/2085/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:13:p:2085-:d:1686779

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().