Hybrid feature- selection using group search particle swarm optimizer for plant- leaf classification

  • Authors

    • A. M. Ravishankar Department of CSE, Karpagam Academy of Higher Education, Karpagam University
    • P. Amudhavalli Department of CSE, Karpagam Academy of Higher Education, Karpagam University
    • G. K. D. Prasanna Venkatesan
  • Plant Leaf Classification, Feature Selection, Artificial Neural Networks (ANN), Multilayer Perceptron Neural Network (MLPNN), Particle Swarm Optimization (PSO) and Group Search Optimizer (GSO).
  • Plants are considered to be the important sources for food and medicine. They are critical for the protection of the environment as well. The plant leaves carry information on the species of the plant. This kind of work can describe an approach that is optimal for the selection of the feature subset in classifying the leaves on the basis of a Group Search Optimizer (GSO). Owing to the high level of complexity in selecting optimal features, data classification has become now an important task to analyse the data of leaf images. Here for this work, there is a hybrid algorithm known as the Group Search Particle Swarm Optimization (GSPSO) which is based upon Particle Swarm Optimization (PSO) and here the GSO has been proposed wherein a PSO model along with the GSO model is made use of. A GSPSO combines all advantages in both the algorithms, the high speed of computing in the PSO and the good performance in the GSO. The Fuzzy classifier is that form of the many-valued logic which is derived from the theory of a fuzzy set. A Multilayer Perceptron Neural Network (MLPNN) concept is used for classification. Such techniques are selected as they can provide a training that is faster to solve the problems of pattern recognition by making use of the technique of numerical optimization.



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  • How to Cite

    M. Ravishankar, A., Amudhavalli, P., & K. D. Prasanna Venkatesan, G. (2019). Hybrid feature- selection using group search particle swarm optimizer for plant- leaf classification. International Journal of Engineering & Technology, 7(4), 5909-5916. https://doi.org/10.14419/ijet.v7i4.22670