@article{293,
  author = {Haiman Tian and Shu-Ching Chen and Mei-Ling Shyu},
  title = {Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification},
  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.},
  year = {2020},
  journal = {Information systems frontiers},
  volume = {22},
  chapter = {1053},
  pages = {14},
  month = {10},
  issn = {1572-9419},
  url = {https://par.nsf.gov/biblio/10233983},
  doi = {https://doi.org/10.1007/s10796-020-10023-6},
}