Affiliations: Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran
Correspondence:
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Corresponding author: Mohammad Rahmati, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Tel.: +98 21 6454 2742; Fax: +98 21 6649 5521; E-mail:rahmati@aut.ac.ir
Abstract: Some of the basic algorithms for learning the structure
of Bayesian networks, such as the well-known K2 algorithm, require a prior
ordering over the nodes as part of the input. It is well known that the
accuracy of the K2 algorithm is highly sensitive to the initial ordering. In
this paper, we introduce the aggregation of ordering information provided by
multiple experts to obtain a more robust node ordering. In order to reduce
the effect of novice participants, the accuracy of each person is used in
the aggregation process. The accuracies of participants, not known in
advance, are estimated by the expectation maximization algorithm. Any
possible contradictions occurred in the resulting aggregation are resolved
by modelling the result as a directed graph and avoiding the cycles in this
graph. Finally, the topological order of this graph is used as the initial
ordering in the K2 algorithm. The experimental results demonstrate the
effectiveness of the proposed method in improving the structure learning
process.
