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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford: Oxford University Press.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281–305.
Chang, W.L. (2019). The impact of emotion: a blended model to estimate influence on social media. Information Systems Frontiers, 21(5), 1137–1151.
Chen, C., Zhu, Q., Lin, L., & Shyu, M.L. (2013). Web media semantic concept retrieval via tag removal and model fusion. ACM Transactions on Intelligent Systems and Technology, 4(4), 61.
Chen, S.C., & Kashyap, R.L. (2001). A spatio-temporal semantic model for multimedia database systems and multimedia information systems. IEEE Transactions on Knowledge and Data Engineering, 13(4), 607–622.
Chen, S.C., Rubin, S.H., Shyu, M.L., & Zhang, C. (2006). A dynamic user concept pattern learning framework for content-based image retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(6), 772–783.
Chen, S.C., Shyu, M.L., & Kashyap, R. (1998). Augmented transition network as a semantic model for video data. International Journal of Networking and Information Systems, 3(3), 9–25.
Chen, S.C., Shyu, M.L., & Zhang, C. (2005). Innovative shot boundary detection for video indexing. In Video data management and information retrieval. IGI Global (pp. 217–236).
Chen, S.C., Shyu, M.L., Zhang, C., & Kashyap, R.L. (2001). Identifying overlapped objects for video indexing and modeling in multimedia database systems. International Journal on Artificial Intelligence Tools, 10(04), 715–734.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In The IEEE conference on computer vision and pattern recognition (pp. 770–778).
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR arXiv:1704.04861.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K.Q. (2017). Densely connected convolutional networks. In The IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
Lameski, P., Zdravevski, E., Mingov, R., & Kulakov, A. (2015). SVM parameter tuning with grid search and its impact on reduction of model over-fitting. In Rough sets, fuzzy sets, data mining, and granular computing (pp. 464–474): Springer.
Li, H., He, F., Liang, Y., & Quan, Q. (2019). A dividing-based many-objective evolutionary algorithm for large-scale feature selection. Soft Computing, 1–20.
Li, X., Chen, S.C., Shyu, M.L., & Furht, B. (2002). Image retrieval by color, texture, and spatial information. In The 8th international conference on distributed multimedia systems (pp. 152–159).
Lin, L., & Shyu, M.L. (2010). Weighted association rule mining for video semantic detection. International Journal of Multimedia Data Engineering and Management (IJMDEM), 1(1), 37–54.
Mania, H., Guy, A., & Recht, B. (2018). Simple random search provides a competitive approach to reinforcement learning. CoRR arXiv:1803.07055.
Mitchell, T.M. (1997). Machine learning.
Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). Pruning convolutional neural networks for resource efficient transfer learning. CoRR arXiv:1611.06440.
Mukherjee, S. (2020). Emerging frontiers in smart environment and healthcare–A vision. Information Systems Frontiers, 22(1), 23–27.
Pan, S.J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.L., Chen, S.C., & Iyengar, S. (2018). A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 92.
Pouyanfar, S., Tao, Y., Tian, H., Chen, S.C., & Shyu, M.L. (2019). Multimodal deep learning based on multiple correspondence analysis for disaster management. World Wide Web, 22(5), 1893–1911.
Pouyanfar, S., Yang, Y., Chen, S.C., Shyu, M.L., & Iyengar, S. (2018). Multimedia big data analytics: a survey. ACM Computing Surveys, 51(1), 10.
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R.M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.
Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian optimization of machine learning algorithms. In Advances in neural information processing systems (pp. 2951–2959).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In The IEEE conference on computer vision and pattern recognition (pp. 2818–2826).
Tian, H., & Chen, S.C. (2017). Mca-nn: multiple correspondence analysis based neural network for disaster information detection. In IEEE third international conference on multimedia big data (bigMM) (pp. 268–275): IEEE.
Tian, H., Chen, S.C., & Shyu, M.L. (2019). Genetic algorithm based deep learning model selection for visual data classification. In The 20th international conference on information reuse and integration for data science (pp. 127–134): IEEE.
Tian, H., Chen, S.C., Shyu, M.L., & Rubin, S. (2019). Automated neural network construction with similarity sensitive evolutionary algorithms. In The 20th international conference on information reuse and integration for data science (pp. 283–290): IEEE.
Tian, H., Pouyanfar, S., Chen, J., Chen, S.C., & Iyengar, S.S. (2018). Automatic convolutional neural network selection for image classification using genetic algorithms. In The IEEE international conference on information reuse and integration (pp. 444–451): IEEE.
Tian, H., Tao, Y., Pouyanfar, S., Chen, S.C., & Shyu, M.L. (2019). Multimodal deep representation learning for video classification. World Wide Web, 22(3), 1325–1341.
Tian, H., Zheng, H.C., & Chen, S.C. (2018). Sequential deep learning for disaster-related video classification. In IEEE conference on multimedia information processing and retrieval (MIPR) (pp. 106–111): IEEE.
Wang, D., & Zheng, T.F. (2015). Transfer learning for speech and language processing. In The asia-pacific signal and information processing association annual summit and conference (pp. 1225–1237): IEEE.
Winston, P.H. (1992). Artificial intelligence.
Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. In Feature extraction, construction and selection (pp. 117–136): Springer.
Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102.
Zhu, Q., Lin, L., Shyu, M.L., & Chen, S.C. (2011). Effective supervised discretization for classification based on correlation maximization. In IEEE international conference on information reuse & integration (pp. 390–395): IEEE.
Zhu, W., Cui, P., Wang, Z., & Hua, G. (2015). Multimedia big data computing. IEEE Multimedia, 22(3), 96–c3.
Acknowledgements
This research is partially supported by NSF CNS-1952089 and OIA-1937019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tian, H., Chen, SC. & Shyu, ML. Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification. Inf Syst Front 22, 1053–1066 (2020). https://doi.org/10.1007/s10796-020-10023-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-020-10023-6