an immune clonal selection algorithm for synthetic signature generation

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ID: 190145
2014
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Abstract
The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmented r-continuous bits matching rule and P-receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification.
Reference Key
song2014mathematicalan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Mofei Song;Zhengxing Sun
Journal journal of power sources
Year 2014
DOI
10.1155/2014/324645
URL
http://dx.doi.org/10.1155/2014/324645
Keywords
civil engineering (general)mathematics engineering (general)

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