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A New Distance Metric Based on Class-Space Reduction

  • Byungjoon Park
  • Sejong Oh
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)

Abstract

The ultimate goal of research regarding classification is to improve accuracy. Classification accuracy highly depends on overlapping areas among classes of the dataset. In general, a wider overlap area produces less classification accuracy. In this study, we suggest a new distance metric based on class-space reduction to improve classification accuracy. Proposed distance metric has same effect to rescale training/test data by moving data points in the direction of the center point of the class that the data points belong to. By conducting experiments using real datasets, we confirmed that many cases of new dataset generated by class-space reduction improved the classification accuracy for some classification algorithms.

Keywords

Distance metric Distance metric learning Classification Preprocessing Class-space reduction Bioinformatics 

Notes

Acknowledgments

This study was supported by grant No. R31-2008-000-10069-0 from the World Class University (WCU) project of the Ministry of Education, Science & Technology (MEST) and the Korea Science and Engineering Foundation (KOSEF).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Nanobiomedical ScienceDankook UniversityCheonanRepublic of Korea

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