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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Summary: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 essential for capturing class-dependent uncertainty. To address this challenge, this paper presents two key contributions. First, the conformal dual uncertainty metric (CDUM), derived from conformal prediction, is proposed as a novel uncertainty criterion. CDUM quantifies class-wise uncertainty by capturing class relationships from two perspectives: the influence of other classes on the current class and the dominance of the current class over others. Second, the conformal deep forest (CDForest), an uncertainty-aware deep model, is proposed by incorporating three CDUM-based modules into the deep forest (DF) architecture. Leveraging these CDUM-based modules, CDForest achieves three core capabilities: (1) layer-wise uncertainty filtering to reduce high-uncertainty features, (2) class-wise weighted averaging to refine ensemble predictions, and (3) class-wise aggregation for conformal predictors to highlight class-specific differences and facilitate more precise uncertainty quantification. Extensive experiments demonstrate that CDForest significantly outperforms state-of-the-art models in terms of classification accuracy, robustness against noise, and decision reliability, further confirming its effectiveness as a highly competitive solution for uncertainty-aware classification tasks.
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ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00175-3