A Novel Approach on Behavior of Sleepy Lizards Based on K-Nearest Neighbor Algorithm

  • Lin-Lin Tang
  • Jeng-Shyang Pan
  • XiaoLv Guo
  • Shu-Chuan Chu
  • John F. Roddick
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 526)

Abstract

The K-Nearest Neighbor algorithm is one of the commonly used methods for classification in machine learning and computational intelligence. A new research method and its improvement for the sleepy lizards based on the K-Nearest Neighbor algorithm and the traditional social network algorithms are proposed in this chapter. The famous paired living habit of sleepy lizards is verified based on our proposed algorithm. In addition, some common population characteristics of the lizards are also introduced by using the traditional social net work algorithms. Good performance of the experimental results shows efficiency of the new research method.

Keywords

Social network analysis (SNA) K-Nearest neighbor (KNN) algorithm Sleep lizard Computational intelligence 

Notes

Acknowledgments

The authors would like to thank the reviewers for providing very helpful comments and suggestions. The authors would also like to thank for the support from the project named Research on Multiple Description Coding Frames with Watermarking Techniques in Wavelet Domain which belongs to the NSFC (National Natural Science Foundation of China) with the Grant number 61202456.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lin-Lin Tang
    • 1
  • Jeng-Shyang Pan
    • 1
  • XiaoLv Guo
    • 1
  • Shu-Chuan Chu
    • 2
  • John F. Roddick
    • 2
  1. 1.Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.School of Computer Science, Engineering and MathematicsFlinders University of South AustraliaAdelaideSouth Australia

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