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Supervised Rank Normalization with Training Sample Selection

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2015, 20(1), pp.21-28
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Gyeongyong Heo 1 Choi Hun ORD ID 1 Youn Joo Sang 1

1동의대학교

Accredited

ABSTRACT

Feature normalization as a pre-processing step has been widely used to reduce the effect of differentscale in each feature dimension and error rate in classification. Most of the existing normalization methods,however, do not use the class labels of data points and, as a result, do not guarantee the optimality ofnormalization in classification aspect. A supervised rank normalization method, combination of ranknormalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique forincreasing classification accuracy by removing noisy samples and can be applied in supervisednormalization method. Two sample selection measures based on the classes of neighboring samples andthe distance to neighboring samples were proposed and both of them showed better results than previoussupervised rank normalization method.

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