Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification
Résumé
In this paper, we discuss simple methods for identification and handling of almost-deterministic relationships (ADR) in automatic constraint-based Bayesian network structure discovery. The problem with ADR is that conditional independence tests become unreliable when the conditional set almost-determine one of the variables in the test. Such errors have usually a cascading effect that causes many errors in the final graph. Several methods for identification and handling of ADR are discussed to provide insight into their advantages and disadvantages. The methods are applied on standard benchmarks to recover the original structure from data in order to assess their capabilities. We then discuss efforts to apply ours findings to Nasopharyngeal Carcinoma (NPC) survey data. The aim is to help identify the important risk factors involved in the NPC cancer.
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