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Supervised Rank Normalization with Training Sample Selection
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 Title & Authors
Supervised Rank Normalization with Training Sample Selection
Heo, Gyeongyong; Choi, Hun; Youn, Joo-Sang;
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 Abstract
Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale 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 of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization 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 for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.
 Keywords
Feature normalization;Rank Normalization;Supervised learning;Training sample selection;
 Language
Korean
 Cited by
1.
Hybrid Pattern Recognition Using a Combination of Different Features,;

한국컴퓨터정보학회논문지, 2015. vol.20. 11, pp.9-16 crossref(new window)
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