Abstract
Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chakraborty, B.: Genetic algorithm with fuzzy fitness function for feature selection. In: Proceedings of the IEEE International Symposium on Industrial Electronics, pp. 315–319 (2002)
Sural, S., Das, P.K.: A genetic algorithm for feature selection in a neuro-fuzzy OCR system. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, pp. 987–991 (2001)
Lanzi, P.L.: Fast feature selection with genetic algorithms: a filter approach. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 537–540 (1997)
John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problems. In: Proceedings of 11th international conference on machine learning, pp. 121–129 (1994)
Shi, D.M., Shu, W.H., Liu, H.T.: Feature selection for handwritten Chinese character recognition based on genetic algorithms . In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4201–4206 (1998)
Koza, J.R.: Survey of genetic algorithm and genetic programming. In: Proceedings of the 1995 Microelectronics Communications Technology Producing Quality Products Mobile and Portable Power Emerging Technologies, pp. 589–594 (1995)
Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey, Computer, vol. 27(6), pp. 17–26 (1994)
Hey, T.: Quantum Computing: an introduction. Computing and Control Engineering Journal 10(3), 105–112 (1996)
Narayanan, A.: Quantum computing for beginners. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 2231–2238 (1999)
Han, K.H., Kim, J.H.: Genetic quantum algorithm and its application to combinatorial optimization problems. In: Proceedings of the 2000 IEEE Conference on Evolutionary Computation, pp. 1354–1360 (2000)
Zhang, G.P.: Neural networks for classification: a survey. IEEE Trans. on System, Man, and Cybernetics-Part C: Application and Reviews. 30(4), 451–462 (2000)
Kavalov, D., Kalinin, V.: Neural network surface acoustic wave RF signal processor for digital modulation recognition. IEEE Trans. on ultrasonics, ferroelectrics, and frequency control 49(9), 1280–1290 (2002)
Riedmiller, M., Braun, H.: A direct adaptive method for faster back propagation learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Zhang, G.X., Jin, W.D., Hu, L.Z.: Fractal feature extraction of radar emitter signals. In: Proceedings of the third Asia-Pacific conference on Environmental Electromagnetics, pp. 161–164 (2003)
Zhang, G.X., Jin, W.D., Hu, L.Z.: Complexity feature extraction of radar emitter signals. In: Proceedings of the third Asia-Pacific conference on Environmental Electromagnetics, pp. 495–498 (2003)
Zhang, G.X., Rong, H.N., Jin, W.D., Hu, L.Z.: Radar emitter signal recognition based on resemblance coefficient features. LNCS, vol. 6. Springer, Berlin (2004) (to appear)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, G., Hu, L., Jin, W. (2004). Quantum Computing Based Machine Learning Method and Its Application in Radar Emitter Signal Recognition. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-27774-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22555-3
Online ISBN: 978-3-540-27774-3
eBook Packages: Springer Book Archive