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New Information Technology Study Results Reported from J.T. Liu et al

February 4, 2014

By a News Reporter-Staff News Editor at Information Technology Newsweekly -- New research on Information Technology is the subject of a report. According to news reporting originating in Wuhan, People's Republic of China, by VerticalNews journalists, research stated, "The problem of learning conditional preference networks (CP-nets) from a set of examples has received great attention recently. However, because of the randomicity of the users' behaviors and the observation errors, there is always some noise making the examples inconsistent, namely, there exists at least one outcome preferred over itself (by transferring) in examples."

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, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA. (Institute of Electrical and Electronics Engineers -; IEEE Transactions on Knowledge and Data Engineering -

Our news correspondents report that additional information may be obtained by contacting J.T. Liu, Air Force Radar Academy, Dept. of Informat Counterwork, Wuhan 430010, People's Republic of China. Additional authors for this research include Y. Xiong, C.H. Wu, Z.J. Yao and W.Y. Liu.

Keywords for this news article include: Asia, Wuhan, Information Technology, People's Republic of China

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Source: Information Technology Newsweekly

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