Terahertz Frequency Domain Spectroscopy Identification System Based on Decision Trees

• Authors: R. Ryniec , P. Zagrajek , N. Pałka
• Languages of publication: 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

42.81.Bm; 42.81.Cn; 42.81.Dp

Discipline

• 42.81.Dp: Propagation, scattering, and losses; solitons
• 42.81.Cn: Fiber testing and measurement of fiber parameters
• 42.81.Bm: Fabrication, cladding, and splicing

• Journal: Acta Physica Polonica A
• Year: 2012
• Volume: 122
• Issue: 5
• Pages: 891-895

Dates

published

2012-11

Contributors

author

R. Ryniec

• Institute of Optoelectronics, Military University of Technology, S. Kaliskiego 2, 00-908 Warsaw, Poland

author

P. Zagrajek

• Institute of Optoelectronics, Military University of Technology, S. Kaliskiego 2, 00-908 Warsaw, Poland

author

N. Pałka

• 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)

Identifiers

DOI

10.12693/APhysPolA.122.891