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Training fuzzy support vector machines by using boundary of rough set

Published: 12 June 2009 Publication History

Abstract

Support Vector Machines (SVMs) are statistical learning methods based on two-class problems and exist unclassifiable regions when they are extended to multi-class problems. In order to reduce unclassifiable regions, S. Abe and T. Inoue proposed the improved multi-class SVMs called Fuzzy Support Vector Machines (FSVMs) by which the unclassifiable regions are reduced. In this paper, we train FSVMs by using the training data lying in the boundary of rough set. Firstly, the whole training set is divided into some equivalence classes by transforming all attribute values into discrete ones. Secondly, the lower approximation sets of the training data with the same categories are obtained by the formed equivalence classes. Thirdly, the boundary induced by the whole training set and the lower approximation sets is selected to form FSVMs. The experimental results on classic benchmark data sets show that the proposed learning machines can downsize the number of training data and achieve the higher predictions.

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cover image ACM Conferences
GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
June 2009
1112 pages
ISBN:9781605583266
DOI:10.1145/1543834

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2009

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Author Tags

  1. boundary
  2. equivalence class
  3. fuzzy support vector machines
  4. rough set
  5. support vector machines

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