A Review on Bayesian Methods for Uncertainty Quantification in Machine Learning Models Enhancing Predictive Accuracy and Model Interpretability

被引：2

作者：

Jiet, Moses Makuei ^{[1
]}

Verma, Prateek ^{[2
]}

Kamble, Aahash ^{[3
]}

Puri, Chetan ^{[1
]}

机构：

[1] Datta Meghe Inst Higher Educ & Res, Fac Engn & Technol, Dept Comp Sci & Design, Wardha 442001, Maharashtra, India

[2] Datta Meghe Inst Higher Educ & Res, Fac Engn & Technol, Dept Artificial Intelligence & Machine Learning, Wardha 442001, Maharashtra, India

[3] Datta Meghe Inst Higher Educ & Res, Fac Engn & Technol, Dept Artificial Intelligence & Data Sci, Wardha 442001, Maharashtra, India

来源：

2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024 | 2024年

关键词：

Bayesian Methods; Machine Learning; Predictive Accuracy; Model Interpretability; Uncertainty Analysis;

D O I：

10.1109/ICOICI62503.2024.10696308

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This research study reviews the statistical fundamentals of machine learning with a focus on Bayesian methods to quantify the uncertainty in model predictions. Bayesian statistics provides a framework for incorporating prior knowledge, updating beliefs, and expressing uncertainty in predictions. This research study will explore Bayesian techniques applied to various aspects of machine learning, including regression, classification, deep learning, and ensemble methods.

引用

页码：1671 / 1675

页数：5

共 22 条

[1] A review of uncertainty quantification in deep learning: Techniques, applications and challenges [J].

Abdar, Moloud ;

Pourpanah, Farhad ;

Hussain, Sadiq ;

Rezazadegan, Dana ;

Liu, Li ;

Ghavamzadeh, Mohammad ;

Fieguth, Paul ;

Cao, Xiaochun ;

Khosravi, Abbas ;

Acharya, U. Rajendra ;

Makarenkov, Vladimir ;

Nahavandi, Saeid .

INFORMATION FUSION, 2021, 76 :243-297

[2] Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach [J].

Abdullah, Abdullah A. ;

Hassan, Masoud M. ;

Mustafa, Yaseen T. .

HELIYON, 2024, 10 (02)

[3]

Amrani N. B., 2019, Journal of Konbin, V49, DOI [10.2478/jok-2019-0045, DOI 10.2478/JOK-2019-0045]

[4]

Foong A. Y., 2020, Advances in Neural Information Processing Systems

[5]

Izmailov P., 2019, P MACH LEARN RES

[6] Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications [J].

Kabir, H. M. Dipu ;

Khosravi, Abbas ;

Hosen, Mohammad Anwar ;

Nahavandi, Saeid .

IEEE ACCESS, 2018, 6 :36218-36234

[7] Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants [J].

Kimei, Erica Hargety ;

Nyambo, Devotha G. ;

Mduma, Neema ;

Kaijage, Shubi .

SUSTAINABILITY, 2024, 16 (05)

[8] Bayesian learning for neural networks: an algorithmic survey [J].

Magris, Martin ;

Iosifidis, Alexandros .

ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (10) :11773-11823

[9] Epistemic uncertainty quantification in deep learning classification by the Delta method [J].

Nilsen, Geir K. ;

Munthe-Kaas, Antonella Z. ;

Skaug, Hans J. ;

Brun, Morten .

NEURAL NETWORKS, 2022, 145 :164-176

[10]

Phan B. T., 2019, Bayesian Deep Learning and Uncertainty in Computer Vision

← 1 2 3 →