| Texto completo | |
| Autor(es): |
Gross, Tadeu Junior
[1]
;
Bessani, Michel
[2]
;
Darwin Junior, Willian
[1]
;
Araujo, Renata Bezerra
[3]
;
Carvalho Vale, Francisco Assis
[4]
;
Maciel, Carlos Dias
[1]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Comp & Elect Engn Dept, Sao Carlos, SP - Brazil
[2] Univ Fed Minas Gerais, Elect Engn Dept, Belo Horizonte, MG - Brazil
[3] Univ Fed Sao Carlos, Nursing Dept, Sao Carlos, SP - Brazil
[4] Univ Fed Sao Carlos, Med Dept, Sao Carlos, SP - Brazil
Número total de Afiliações: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | KNOWLEDGE-BASED SYSTEMS; v. 175, p. 36-49, JUL 1 2019. |
| Citações Web of Science: | 0 |
| Resumo | |
Structural learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One approach is the generation of multiple approximate structures and then reduce the ensemble to a representative structure. This can be performed by using the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this paper, an analogy with an adapted one-dimensional random-walk was used for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across the synthetic datasets applying the Matthews Correlation Coefficient (MCC) as the performance metric. In the experiments using a recent medical dataset, the resulting BN from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. (C) 2019 Elsevier B.V. All rights reserved. (AU) | |
| Processo FAPESP: | 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente |
| Beneficiário: | Marco Henrique Terra |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |