Investigation of the K2 algorithm in learning bayesian network classifiers

Boaz Lerner, Roy Malka

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


We experimentally study the K2 algorithm in learning a Bayesian network (BN) classifier for image detection of cytogenetic abnormalities. Starting from an initial BN structure, the K2 algorithm searches the BN structure space and selects the structure maximizing the K2 metric. To improve the accuracy of the K2-based BN classifier, we investigate the K2 algorithm initial ordering, search procedure, and metric. We find that BN structures learned using random initial orderings, orderings based on expert knowledge, or a scatter criterion are comparable and lead to similar classification accuracies. Replacing the K2 search with hill-climbing search improves the accuracy as does the inclusion of hidden nodes in the BN structure. Also, we demonstrate that though the maximization of the K2 metric solicits structures providing improved inference, these structures contribute to only limited classification accuracy.

Original languageEnglish
Pages (from-to)74-96
Number of pages23
JournalApplied Artificial Intelligence
Issue number1
StatePublished - 1 Jan 2011

ASJC Scopus subject areas

  • Artificial Intelligence


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