Conformal deep forest for uncertainty-aware classification

Uncertainty in deep learning models significantly impacts their performance, robustness, and reliability, making explicit uncertainty quantification a critical research focus. However, existing methods often fail to incorporate relationships between classes into uncertainty quantification, which are...

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Bibliographic Details
Published in:	Journal of King Saud University. Computer and information sciences Vol. 37; no. 6; pp. 155 - 33
Main Authors:	Zhang, Jing, Qiu, Yunfei, Dong, Libo
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.08.2025 Springer Nature B.V Springer
Subjects:	Class relationship Class-wise uncertainty Classification Computer Imaging Computer Science Conformal prediction Database Management Deep forest Deep learning Machine Learning Modules Neural networks Original Paper Pattern Recognition and Graphics Reliability Robustness (mathematics) Software Engineering/Programming and Operating Systems Systems and Data Security Theory of Computation Uncertainty Vision Class-wise uncertainty Deep forest Class relationship Conformal prediction
ISSN:	1319-1578, 2213-1248, 1319-1578
Online Access:	Get full text
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