Consistency of weighted feature set and polyspectral kernels in individual communication transmitter identification

Abstract

This paper presents a method using support vector machine with polyspectral kernels for classification of individual transmitters. Then, the neighborhood-rough-set-based weighted feature set is proposed. The experiments of the algorithms mentioned above indicate that they have consistency, which raises a new weighted kernel. The experiment shows that better classification rate can be achieved.

Keywords

polyspectral kernel / support vector machine (SVM) / neighborhood rough set / weighted feature set / weighted kernel

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Acknowledgements

This work was supported by the National High Technology Research and Development Program of China (Grant No. 2009AA01Z430), the Natural Science Foundation of Beijing (No. 9092009), and the National Science and Technology Major Program (2009ZX03004-003-03).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg