The news reporters obtained a quote from the research, "Existing CP-nets learning methods cannot handle inconsistent examples. In this work, we introduce the model of learning consistent CP-nets from inconsistent examples and present a method to solve this model. We do not learn the CP-nets directly. Instead, we first learn a preference graph from the inconsistent examples, because dominance testing and consistency testing in preference graphs are easier than those in CP-nets. The problem of learning preference graphs is translated into a 0-1 programming and is solved by the branch-and-bound search. Then, the obtained preference graph is transformed into a CP-net equivalently, which can entail a subset of examples with maximal sum of weight. Examples are given to show that our method can obtain consistent CP-nets over both binary and multivalued variables from inconsistent examples."
According to the news reporters, the research concluded: "The proposed method is verified on both simulated data and real data, and it is also compared with existing methods."
For more information on this research see: Learning Conditional Preference Networks from Inconsistent Examples. IEEE Transactions on Knowledge and Data Engineering, 2014;26(2):376-390. IEEE Transactions on Knowledge and Data Engineering can be contacted at: Ieee Computer Soc,
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