Using linear programming for weights identification of generalized bonferroni means in R Beliakov, Gleb and James, Simon 2012, Using linear programming for weights identification of generalized bonferroni means in R, in MDAI 2012 : Modeling decisions for artificial intelligence : 9th International Conference, Girona, Catalonia, Spain, November 2012 : proceedings, Springer, Berlin, Germany, pp. 35-44, doi: 10.1007/978-3-642-34620-0_5. Attached Files Name Description MIMEType Size Downloads beliakove-usinglinear-post-2012.pdf Authors' post print application/pdf 380.80KB 81 Title Using linear programming for weights identification of generalized bonferroni means in R Author(s) Beliakov, Gleb orcid.org/0000-0002-9841-5292 James, Simon orcid.org/0000-0003-1150-0628 Conference name Modeling Decisions for Artificial Intelligence. Conference (9th : 2012 : Catalonia, Spain) Conference location Catalonia, Spain Conference dates 21-23 Nov. 2012 Title of proceedings MDAI 2012 : Modeling decisions for artificial intelligence : 9th International Conference, Girona, Catalonia, Spain, November 2012 : proceedings Editor(s) Torra, Vicenc Narukawa, Yasuo Lopez, Beatriz Villaret, Mateu Publication date 2012 Series Lecture Notes in Artificial Intelligence ; v.7647 Conference series Modeling Decisions for Artificial Intelligence Conference Start page 35 End page 44 Total pages 10 Publisher Springer Place of publication Berlin, Germany Keyword(s) aggregation functions generalized Bonferroni mean least absolute deviation (LAD) fitting means weights identification Summary The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset. ISBN 9783642346194 ISSN 0302-9743 Language eng DOI 10.1007/978-3-642-34620-0_5 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2012, Springer Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30051350 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 18 Mar 2013, 09:12:40 EST Mon, 18 Mar 2013, 09:27:31 EST Mon, 18 Mar 2013, 09:29:01 EST Thu, 23 May 2013, 12:57:06 EST Thu, 23 May 2013, 13:09:10 EST Fri, 24 May 2013, 08:32:07 EST Mon, 21 Oct 2013, 14:26:10 EST Fri, 11 Jul 2014, 09:43:34 EST Fri, 11 Jul 2014, 09:44:32 EST Mon, 21 Jul 2014, 12:37:12 EST Fri, 06 Feb 2015, 11:08:07 EST Wed, 04 Mar 2015, 08:07:00 EST Wed, 04 Mar 2015, 08:10:48 EST Mon, 09 Mar 2015, 09:53:25 EST Mon, 09 Mar 2015, 09:54:37 EST Fri, 09 Oct 2015, 17:07:12 EST Mon, 12 Oct 2015, 15:15:45 EST Wed, 18 Nov 2015, 06:48:28 EST Tue, 05 Dec 2017, 12:39:30 EST Fri, 07 Sep 2018, 16:25:41 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 11 times in Scopus Search Google Scholar Access Statistics: 674 Abstract Views, 89 File Downloads  -  Detailed Statistics Created: Mon, 18 Mar 2013, 09:12:40 EST