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A Context-Aware Fitness Function Based on Feature Selection for Evolutionary Learning of Characteristic Graph Patterns

  • Fumiya TokuharaEmail author
  • Tetsuhiro Miyahara
  • Tetsuji Kuboyama
  • Yusuke Suzuki
  • Tomoyuki Uchida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10191)

Abstract

We propose a context-aware fitness function based on feature selection for evolutionary learning of characteristic graph patterns. The proposed fitness function estimates the fitness of a set of correlated individuals rather than the sum of fitness of the individuals, and specifies the fitness of an individual as its contribution degree in the context of the set. We apply the proposed fitness function to our evolutionary learning, based on Genetic Programming, for obtaining characteristic graph patterns from positive and negative graph data. We report some experimental results on our evolutionary learning of characteristic graph patterns, using the context-aware fitness function and a previous fitness function ignoring context.

Notes

Acknowledgments

We would like to thank the anonymous referees for their helpful comments. This work was partially supported by JSPS KAKENHI Grant Numbers JP15K00312 and JP26280090.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fumiya Tokuhara
    • 1
    Email author
  • Tetsuhiro Miyahara
    • 1
  • Tetsuji Kuboyama
    • 2
  • Yusuke Suzuki
    • 1
  • Tomoyuki Uchida
    • 1
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan
  2. 2.Computer CentreGakushuin UniversityTokyoJapan

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