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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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.

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