Lorentzian Distance Classifier for Multiple Features

Yerzhan Kerimbekov, Hasan Şakir Bilge

Abstract

Machine Learning is one of the frequently studied issues in the last decade. The major part of these research area is related with classification. In this study, we suggest a novel Lorentzian Distance Classifier for Multiple Features (LDCMF) method. The proposed classifier is based on the special metric of the Lorentzian space and adapted to more than two features. In order to improve the performance of Lorentzian Distance Classifier (LDC), a new Feature Selection in Lorentzian Space (FSLS) method is improved. The FSLS method selects the significant feature pair subsets by discriminative criterion which is rebuilt according to the Lorentzian metric. Also, in this study, a data compression (pre-processing) step is used that makes data suitable in Lorentzian space. Furthermore, the covariance matrix calculation in Lorentzian space is defined. The performance of the proposed classifier is tested through public GESTURE, SEEDS, TELESCOPE, WINE and WISCONSIN data sets. The experimental results show that the proposed LDCMF classifier is superior to other classical classifiers.

References

  1. Louridas P., Ebert C., 2016. Machine Learning, in IEEE Software, 33 (5), pp. 110-115.
  2. Wang et al., 2016. Nonlinearity Mitigation Using a Machine Learning Detector Based on k -Nearest Neighbours, in IEEE Photonics Technology Letters, 28 (19), pp. 2102-2105.
  3. Bkassiny, M., Li, Y., Jayaweera, S. K., 2013. A survey on machine-learning techniques in cognitive radios. IEEE Communications Surveys & Tutorials, 15(3), 1136- 1159.
  4. Theodoridis S., Koutroumbas K., 2009. Pattern Recognition, Elsevier, 4th ed.
  5. Kerimbekov, Y., Bilge, H. S., Ugurlu, H. H., 2016. The use of Lorentzian distance metric in classification problems. Pattern Recognition Letters, 84, 170-176.
  6. Bilge, H. S., Ke?imbekov, Y., 2015, May. Classification with Lorentzian distance metric. In 2015 23nd Signal Processing and Communications Applications Conference (SIU), pp. 2106-2109. IEEE.
  7. Bilge, H. S., Kerimbekov, Y., Ugurlu, H. H., 2015, September. A new classification method by using Lorentzian distance metric. In Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on, pp. 1-6. IEEE.
  8. Gündogan, H., & Kecilioglu, O., 2006. Lorentzian matrix multiplication and the motions on Lorentzian plane. Glasnik matematicki, 41(2), 329-334.
  9. R. Brualdi, 2010., Introductory Combinatorics, Pearson Prentice Hall, 5th ed.
  10. Marcus, M., Minc, H., 1992. A survey of matrix theory and matrix inequalities, Courier Corporation.
  11. Lichman, M., 2013. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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Paper Citation


in Harvard Style

Kerimbekov Y. and Şakir Bilge H. (2017). Lorentzian Distance Classifier for Multiple Features . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 493-501. DOI: 10.5220/0006197004930501


in Bibtex Style

@conference{icpram17,
author={Yerzhan Kerimbekov and Hasan Şakir Bilge},
title={Lorentzian Distance Classifier for Multiple Features},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={493-501},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006197004930501},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Lorentzian Distance Classifier for Multiple Features
SN - 978-989-758-222-6
AU - Kerimbekov Y.
AU - Şakir Bilge H.
PY - 2017
SP - 493
EP - 501
DO - 10.5220/0006197004930501