Assessing the use of voting methods to improve Bayesian network structure learning
Abu-Hakmeh, Khaldoon Emad
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Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning algorithms on large networks. The selected algorithms then serve as a committee, which then votes on the final network structure. The result is a more selective final network, containing few false positives, with compromised ability to detect all network features.