Work

Expert Bayes: Automatically Refining Manually Built Bayesian Networks

Year: 2014

Type: conference-paper

Abstract: Bayesian network structures are usually built using only the data and starting from an empty network or from a naïve Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers with a very small computational cost, while maintaining most of the intended meaning of the original model.

Source: International Conference on Machine Learning and Applications

DOI: 10.1109/icmla.2014.64

Authors: Ezilda Almeida, Pedro Ferreira, Tiago T.V. Vinhoza, Ines Dutra, Paulo Borges, Yirong Wu, Elizabeth Burnside

Institutions: INESC TEC, Universidade do Porto, University of Wisconsin–Madison

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, Fundação para a Ciência e a Tecnologia, National Cancer Institute, National Institute of Allergy and Infectious Diseases, U.S. National Library of Medicine

Awards: ADE - Detecção de Efeitos Adversos de Drogas, G1578237752, Informatics Techniques to Optimize Mammography for Aging Populations, The Center for Predictive Computational Phenotyping-1 Overall, Integrating Machine Learning and Physician Expertise for Breast Cancer Diagnosis, UW Comprehensive Cancer Center Support

OpenAlex ID: W1967026953

FWCI: 0.775

Cites: 12

Cited by: 11

Related to: 10