Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases (Articolo in rivista) (literal)

Anno

• 2009-01-01T00:00:00+01:00 (literal)

Alternative label

• Meloni A.; Ripoli A.; Positano V.; Landini L. (2009)
  Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases
  in IEEE transactions on information technology in biomedicine
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Meloni A.; Ripoli A.; Positano V.; Landini L. (literal)

Pagina inizio

• 984 (literal)

Pagina fine

• 989 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 13 (literal)

Rivista

• IEEE transactions on information technology in biomedicine (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• In: Ieee Transactions on Information Technology in Biomedicine, vol. 13 pp. 984 - 989. IEEE, 2009. (literal)

Note

• Scopus (literal)
• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto di Fisiologia Clinica, CNR, Pisa; Fondazione G. Monasterio CNR/Regione Toscana, Pisa; Fondazione G. Monasterio CNR/Regione Toscana, Pisa; Department of Information Engineering, University of Pisa, Pisa. (literal)

Titolo

• Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases (literal)

Abstract

• Bayesian networks (BNs) represent one of the most successful tools for medical diagnosis, selection of the optimal treatment, and prediction of the treatment outcome. In this paper, we present an algorithm for BN structure learning, which is a variation of the standard search-and-score approach. The proposed algorithm overcomes the creation of redundant network structures that may include nonsignificant connections between variables. In particular, the algorithm finds what relationships between the variables must be prevented, by exploiting the binarization of a square matrix containing the mutual information (MI) among all pairs of variables. Two different binarization methods are implemented. The first one is based on the maximum relevance minimum redundancy selection strategy. The second one uses a threshold. The MI binary matrix is exploited as a preconditioning step for the subsequent greedy search procedure that optimizes the network score, reducing the number of possible search paths in the greedy search. Our algorithm has been tested on two different medical datasets and compared against the standard search-and-score algorithm as implemented in the DEAL package. (literal)

Prodotto di

• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)
• Istituto di fisiologia clinica (IFC) (Istituto)

Autore CNR

• LUIGI LANDINI (Unità di personale esterno)

Insieme di parole chiave

• Parole chiave di "Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di fisiologia clinica (IFC) (Istituto)
• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)

Autore CNR di

• LUIGI LANDINI (Unità di personale esterno)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• IEEE transactions on information technology in biomedicine (Rivista)

Insieme di parole chiave di

• Parole chiave di "Mutual Information Preconditioning Improves Structure Learning of Bayesian Networks from Medical Databases" (Insieme di parole chiave)