Reasoning over Bayesian Networks using Semantic Artificial Neural Networks

No Thumbnail Available
File version
Author(s)
Batsakis, S
Antoniou, G
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2021
Size
File type(s)
Location
License
Abstract

Representation of application domains, related concepts and their dependencies is often achieved using Bayesian Networks. In Bayesian Networks nodes represent random variables and arcs represent their dependencies. Since inference over Bayesian Networks is a complex task in this work a novel approach for representing and reasoning over Bayesian Networks using Semantically labeled Neural Networks is proposed and evaluated. Using Semantic Neural Networks combines advantages of Neural Networks such as wide adoption and highly optimized implementations while preserving the interpretability of Bayesian Networks which is an important requirement, especially in medical applications. In addition the proposed approach is evaluated over medical datasets with positive results

Journal Title
Conference Title
IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Neural networks
Persistent link to this record
Citation
Batsakis, S; Antoniou, G, Reasoning over Bayesian Networks using Semantic Artificial Neural Networks, IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications, 2021