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A dominant hyperrectangle generation technique of classification using IG partitioning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(1), pp.149-156
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Hyeong-Il Lee 1

1김포대학교

Accredited

ABSTRACT

NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data atthe same time, can reduce the size of the model. It is the optimal distance-based classificationmethod using a matching rule. NGE cross or overlap hyperrectangles generated in the learning hasbeen noted to inhibit the factors. In this paper, We propose the DHGen(Dominant HyperrectangleGeneration) algorithm which avoids the overlapping and the crossing between hyperrectangles,uses interval weights for mixed hyperrectangles to be splited based on the mutual information. TheDHGen improves the classification performance and reduces the number of hyperrectangles byprocessing the training set in an incremental manner The proposed DHGen has been successfully shown to exhibit comparable classificationperformance to k-NN and better result than EACH system which implements the NGE theoryusing benchmark data sets from UCI Machine Learning Repository.

Citation status

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