Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight

Author:

Zhao Yanyang12ORCID,Wang Yawen12,Zhang Dalin3,Gong Yunzhan1

Affiliation:

1. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

2. Guangxi Key Laboratory of Cryptography and Information Security Guilin China

3. School of Software Engineering Beijing Jiaotong University Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference80 articles.

1. BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

2. A review of uncertainty quantification in deep learning: Techniques, applications and challenges

3. Abdar M. Salari S. Qahremani S. Lam H.‐K. Karray F. Hussain S. Khosravi A. Acharya U. R. Makarenkov V. &Nahavandi S.(2021).Uncertaintyfusenet: Robust uncertainty‐aware hierarchical feature fusion with ensemble Monte Carlo dropout for covid‐19 detection.

4. Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

5. Improving deterministic uncertainty estimation in deep learning for classification and regression;Amersfoort J. V.;CoRR,2021

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