Indexing Based Feature Selection by Applying Ant Colony Optimization Method for Improving Web Page Classification

  • Authors

    • A M. James Raj
    • F Sagayaraj Francis
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17882
  • Ant colony optimization, firefly algorithm, particle swarm optimization, cuckoo search algorithm, bat algorithms, wolf search and genetic algorithms or programming.

  • Abstract

    In this information age many research work are carried out in web page classification to acquire the relevant and appropriate information. To be more specific, for enhancing the web page classification to obtain the optimized feature sets are chosen by utilizing the evolutionary algorithms.  Normally, these algorithms are designed by the heuristic principles stimulated by natural evolution. After analyzing the significance of the various evolutionary algorithms deployed by several researchers in this domain so far, this work also intended to apply them to acquire the best solutions (enhanced features). In general, applying the evolutionary algorithms the fittest genes are generated and determined by the fitness function. Once the fittest genes are decided picking up the fittest individual genomes from a population for taking them to the next generations is the challenging task. In this article a novel approach is proposed to choose the best solutions.

     

     

  • References

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  • How to Cite

    M. James Raj, A., & Sagayaraj Francis, F. (2018). Indexing Based Feature Selection by Applying Ant Colony Optimization Method for Improving Web Page Classification. International Journal of Engineering & Technology, 7(3.27), 227-232. https://doi.org/10.14419/ijet.v7i3.27.17882

    Received date: 2018-08-19

    Accepted date: 2018-08-19

    Published date: 2018-08-15