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2012 | 122 | 5 | 891-895
Article title

Terahertz Frequency Domain Spectroscopy Identification System Based on Decision Trees

Content
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EN
Abstracts
EN
The application of pattern recognition methodology within chemistry, biology and other science domains, especially in security systems is becoming more and more important. Many classification algorithms are available in literature but decision trees are the most commonly exploited because of their ease of implementation and understanding in comparison to other classification algorithms. Decision trees are powerful and popular tools for classification and prediction. In contrast to neural networks, decision trees represent rules, which can readily be expressed so that humans can understand them or even directly use in a database. In this paper we present an algorithm of construction of decision trees and a classification rule extraction based on a logical relationship between attributes and a generalized decision function. Moreover, correctness and efficiency of the algorithm was experimentally validated in a terahertz system, where spectra of explosives were measured in reflection configuration.
Keywords
EN
Contributors
author
  • Institute of Optoelectronics, Military University of Technology, S. Kaliskiego 2, 00-908 Warsaw, Poland
author
  • Institute of Optoelectronics, Military University of Technology, S. Kaliskiego 2, 00-908 Warsaw, Poland
author
  • Institute of Optoelectronics, Military University of Technology, S. Kaliskiego 2, 00-908 Warsaw, Poland
References
  • [1] P.F. Tribe, D. Newnham, P. Taday, M. Kemp, Proc. SPIE 5354, 168 (2004)
  • [2] H. Yang, Z. Xu, J. Zhang, J. Cai, in: IEEE, Proc. CASoN, 2010, p. 49
  • [3] D. Brigida, X. Zhang, IEEE Trans. Terahertz Sci. Technol. 2, 493 (2012)
  • [4] R. Ryniec, M. Piszczek, M. Szustakowski, Acta Phys. Pol. A 118, 1235 (2010)
  • [5] N. Palka, Acta. Phys. Pol. A 120, 715 (2011)
  • [6] B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge 1996
  • [7] M. Walesiak, E. Gatnar, Data Statistical Analysis Using R Program, PWN, Warszawa 2009 (in Polish)
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv122n523kz
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