RRS + LS-SVM: a new strategy for “a priori” sample selection

  • Authors:

  • Bernardo Penna Resende de Carvalho;Wilian Soares Lacerda;Antônio de Pádua Braga

  • Affiliations:

  • Federal University of Minas Gerais—UFMG, Computational Intelligence Laboratory, 31270-901, Belo Horizonte, MG, Brazil;Federal University of Lavras—UFLA, Department of Computing Science, 37200-000, Lavras, MG, Brazil;Federal University of Minas Gerais—UFMG, Department of Electronics Engineering, 31270-901, Belo Horizonte, MG, Brazil

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2007

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Abstract

We present in this work a new Sparse Hybrid Classifier, by using reduced remaining subset (RRS) with least squares support vector machine (LS-SVM). RRS is a sample selection technique based on a modified nearest neighbor rule. It is used in order to choose the best samples to represent each class of a given database. After that, LS-SVM uses the samples selected by RRS as support vectors to find the decision surface between the classes, by solving a system of linear equations. This hybrid classifier is considered as a sparse one because it is able to detect support vectors, what is not possible when using LS-SVM separately. Some experiments are presented to compare the proposed approach with two existent methods that also aim to impose sparseness in LS-SVMs, called LS 2-SVM and Ada-Pinv.