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A Novel FLANN with a Hybrid PSO and GA Based Gradient Descent Learning for Classification

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Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 327))

Abstract

In this paper, it is an attempt to design a PSO & GA based FLANN model (PSO-GA-FLANN) for classification with a hybrid Gradient Descent Learning (GDL). The PSO, GA and the gradient descent search are used iteratively to adjust the parameters of FLANN until the error is less than the required value. Accuracy and convergence of PSO-GA-FLANN is investigated and compared with FLANN, GA-based FLANN and PSO-based FLANN. These models have been implemented and results are statistically analyzed using ANOVA test in order to get significant result. To obtain generalized performance, the proposed method has been tested under 5-fold cross validation.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Bighnaraj Naik, Janmenjoy Nayak & H. S. Behera

Authors
  1. Bighnaraj Naik
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  2. Janmenjoy Nayak
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  3. H. S. Behera
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Corresponding author

Correspondence to Bighnaraj Naik .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Bhubaneswar Engineering College, Bhubaneswar, Odisha, India

    Bhabendra Narayan Biswal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Department of Computer Science and Engineering, University of Kalyanai, Faculty of Engg., Tech. & Management, Kalyanai, West Bengal, India

    J.K. Mandal

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Naik, B., Nayak, J., Behera, H.S. (2015). A Novel FLANN with a Hybrid PSO and GA Based Gradient Descent Learning for Classification. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_84

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  • DOI: https://doi.org/10.1007/978-3-319-11933-5_84

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11932-8

  • Online ISBN: 978-3-319-11933-5

  • eBook Packages: EngineeringEngineering (R0)

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