IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v22y2020i5d10.1007_s10796-020-10023-6.html
   My bibliography  Save this article

Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification

Author

Listed:
  • Haiman Tian

    (Florida International University)

  • Shu-Ching Chen

    (Florida International University)

  • Mei-Ling Shyu

    (University of Miami)

Abstract

Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution Programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.

Suggested Citation

  • Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 2020. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 22(5), pages 1053-1066, October.
  • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10023-6
    DOI: 10.1007/s10796-020-10023-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-020-10023-6
    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/s10796-020-10023-6?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. Lin Lin & Mei-Ling Shyu, 2010. "Weighted Association Rule Mining for Video Semantic Detection," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 1(1), pages 37-54, January.
    2. Satyen Mukherjee, 2020. "Emerging Frontiers in Smart Environment and Healthcare – A Vision," Information Systems Frontiers, Springer, vol. 22(1), pages 23-27, February.
    3. Wei-Lun Chang, 2019. "The Impact of Emotion: A Blended Model to Estimate Influence on Social Media," Information Systems Frontiers, Springer, vol. 21(5), pages 1137-1151, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. R. Elakkiya & Pandi Vijayakumar & Marimuthu Karuppiah, 2021. "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking," Information Systems Frontiers, Springer, vol. 23(6), pages 1369-1383, December.
    2. Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
    3. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.
    4. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.

    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. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 0. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    2. R. Ramesh & H. R. Rao, 2020. "ISF Editorial 2020," Information Systems Frontiers, Springer, vol. 22(1), pages 1-9, February.
    3. Yu Lehe & Gui Zhengxiu, 2021. "Analysis of Enterprise Social Media Intelligence Acquisition Based on Data Crawler Technology," Entrepreneurship Research Journal, De Gruyter, vol. 11(2), pages 3-23, April.
    4. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.
    5. Chao Chen & Mei-Ling Shyu & Shu-Ching Chen, 2016. "Weighted subspace modeling for semantic concept retrieval using gaussian mixture models," Information Systems Frontiers, Springer, vol. 18(5), pages 877-889, October.
    6. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    7. Mohammadreza Mousavizadeh & Mehrdad Koohikamali & Mohammad Salehan & Dam J. Kim, 2022. "An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 24(1), pages 211-231, February.
    8. Grace Fox & Tabitha L. James, 2021. "Toward an Understanding of the Antecedents to Health Information Privacy Concern: A Mixed Methods Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1537-1562, December.

    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:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10023-6. 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.