A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm

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This paper presents results of experiments on some data sets using bagging on the MLEM2 rule induction algorithm. Three different methods of ensemble voting, based on support (a non-democratic voting in which ensembles vote with their strengths), strength only (an ensemble with the largest strength decides to which concept a case belongs) and democratic voting (each ensemble has at most one vote) were used. Our conclusions are that though in most cases democratic voting was the best, it is not significantly better than voting based on support. The strength voting was the worst voting method.

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Cohagan, Clinton; Grzymala-Busse, Jerzy W. & Hippe, Zdzislaw S. March 17, 2010.

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This paper presents results of experiments on some data sets using bagging on the MLEM2 rule induction algorithm. Three different methods of ensemble voting, based on support (a non-democratic voting in which ensembles vote with their strengths), strength only (an ensemble with the largest strength decides to which concept a case belongs) and democratic voting (each ensemble has at most one vote) were used. Our conclusions are that though in most cases democratic voting was the best, it is not significantly better than voting based on support. The strength voting was the worst voting method.

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  • 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010) on the campus of the University of Warsaw, Warsaw, Poland, June 28-30, 2010.

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  • Report No.: KCP-613-8739
  • Grant Number: DE-AC04-01AL66850
  • Office of Scientific & Technical Information Report Number: 1074258
  • Archival Resource Key: ark:/67531/metadc845701

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  • March 17, 2010

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  • May 19, 2016, 9:45 a.m.

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  • June 17, 2016, 3:06 p.m.

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Cohagan, Clinton; Grzymala-Busse, Jerzy W. & Hippe, Zdzislaw S. A Comparison of Three Voting Methods for Bagging with the MLEM2 Algorithm, article, March 17, 2010; United States. (digital.library.unt.edu/ark:/67531/metadc845701/: accessed November 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.