IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v36y2019i2d10.1007_s00357-018-9288-4.html
   My bibliography  Save this article

A Reliable Small Sample Classification Algorithm by Elman Neural Network Based on PLS and GA

Author

Listed:
  • Weikuan Jia

    (Shandong Normal University
    Shandong Normal University)

  • Dean Zhao

    (Shandong Normal University)

  • Ling Ding

    (Shandong Normal University)

  • Yuanjie Zheng

    (Shandong Normal University
    Shandong Normal University
    Shandong Normal University)

Abstract

Aiming at the small sample with high-feature dimension and few numbers will cause a serious problem if simply using the traditional Elman neural network to deal with the small sample; these problems include poor learning ability, the redundancy structure, and incomplete training; these defects will result in lower operating efficiency and poor recognition precision. In this paper, combining the theory of partial least squares (PLS) and genetic algorithm (GA), as well as the nature of Elman neural network, and an optimized Elman neural network classification algorithm based on PLS and GA (PLS-GA-Elman) is established. The new algorithm reduces the feature dimension of small samples by PLS, the relatively ideal low-dimensional data is obtained, and the purpose reduces the neural network’s inputs and simplifies its structure. Using GA to optimize the connection weights, threshold values, and the number of hidden neurons and adopting the optimized way of encoding respectively and evolving simultaneously can improve the neural network incomplete training condition, leading to fewer number of samples and improvement in training speed and generalization ability; this ensures the optimal Elman neural network algorithm. A new algorithm based on twice consecutive optimization was the basis for a precise classification model. The results of experimental analysis illustrate that operating efficiency and classification precision of the new algorithm have been improved.

Suggested Citation

  • Weikuan Jia & Dean Zhao & Ling Ding & Yuanjie Zheng, 2019. "A Reliable Small Sample Classification Algorithm by Elman Neural Network Based on PLS and GA," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 306-321, July.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:2:d:10.1007_s00357-018-9288-4
    DOI: 10.1007/s00357-018-9288-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-018-9288-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-018-9288-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Herbert K.H. Lee, 2007. "Default Priors for Neural Network Classification," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 53-70, June.
    2. Michio Yamamoto & Heungsun Hwang, 2017. "Dimension-Reduced Clustering of Functional Data via Subspace Separation," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 294-326, July.
    3. Zhan Shu & Robert Henson & John Willse, 2013. "Using Neural Network Analysis to Define Methods of DINA Model Estimation for Small Sample Sizes," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 173-194, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    2. Amandine Schmutz & Julien Jacques & Charles Bouveyron & Laurence Chèze & Pauline Martin, 2020. "Clustering multivariate functional data in group-specific functional subspaces," Computational Statistics, Springer, vol. 35(3), pages 1101-1131, September.
    3. Matthieu Marbac & Mohammed Sedki & Tienne Patin, 2020. "Variable Selection for Mixed Data Clustering: Application in Human Population Genomics," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 124-142, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jclass:v:36:y:2019:i:2:d:10.1007_s00357-018-9288-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.