Learning bayesian networks for cytogenetic image classification

Boaz Lerner, Roy Malka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We experimentally learn structures of Bayesian networks classifying signals enabling genetic abnormality diagnosis. Structures learned based on the naive Bayesian classifier, expert knowledge or using the K2 algorithm are compared. Inferiority of the K2-based classifier has motivated an investigation of the algorithm initial ordering, search procedure and metric. Replacing the K2 search with hill-climbing search improves accuracy as does the inclusion of hidden variables into the structure. However, it is proved experimentally that this inferiority of the K2-based classifier is mainly due to the K2 metric soliciting structures having enhanced representability but limited classification accuracy.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages772-775
Number of pages4
Volume2
DOIs
StatePublished - 1 Dec 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 20 Aug 200624 Aug 2006

Conference

Conference18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period20/08/0624/08/06

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