Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/169D34AA-AD65-4316-A28F-DBE0373BD650 | ||
| Year | 2021 | ||
| Type of Item | Conference Publication | ||
| License |
|
||
| Bibliographic Citation | S. Batsakis and G. Antoniou, "Reasoning over Bayesian Networks using Semantic Artificial Neural Networks," presented at the 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), Chania Crete, Greece, 2021, doi: 10.1109/IISA52424.2021.9555501. https://doi.org/10.1109/IISA52424.2021.9555501 | ||
| Appears in Collections |
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.
Copyright © DIAS 2013
