CNN-VSR: A Deep Learning Architecture with Validation-Based Stopping Rule for Time Series Classication

References

• Acharya, U. R., S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, A. Gertych, and R. S. Tan. 2017. A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine 89:389–96. doi:10.1016/j.compbiomed.2017.08.022.
   PubMed Web of Science ®Google Scholar
• Anders, U., and O. Korn. 1999. Model selection in neural networks. Neural Networks 12 (2):309–23. doi:10.1016/S0893-6080(98)00117-8.
   PubMed Web of Science ®Google Scholar
• Antonucci, A., R. De Rosa, A. Giusti, and F. Cuzzolin. 2015. Robust classification of multivariate time series by imprecise hidden Markov models. International Journal of Approximate Reasoning 56:249–63. doi:10.1016/j.ijar.2014.07.005.
   Web of Science ®Google Scholar
• Balkin, S. D., and J. Keith Ord. 2000. Automatic neural network modeling for univariate time series. International Journal of Forecasting 16 (4):509–15. doi:10.1016/S0169-2070(00)00072-8.
   Web of Science ®Google Scholar
• Baydogan, M. G., G. Runger, and E. Tuv. 2013. A bag-of-features framework to classify time series. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (11):2796–802. doi:10.1109/TPAMI.2013.72.
   PubMed Web of Science ®Google Scholar
• Bengio, Y. 2013. Deep learning of representations: Looking forward. In International Conference on Statistical Language and Speech Processing, Berlin, Heidelberg, 1–37. Springer.
   Google Scholar
• Bengio, Y., A. Courville, and P. Vincent. 2013. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8):1798–828. doi:10.1109/TPAMI.2013.50.
   PubMed Web of Science ®Google Scholar
• Chen, H., P. Tino, A. Rodan, and X. Yao. 2014. Learning in the model space for cognitive fault diagnosis. IEEE Transactions on Neural Networks and Learning Systems 25 (1):124–36. doi:10.1109/TNNLS.2013.2256797.
   PubMed Web of Science ®Google Scholar
• Chen, Y., E. Keogh, H. Bing, N. Begum, A. Bagnall, A. Mueen, and G. Batista. 2016. The UCR time series classification archive (2015). www.cs.ucr.edu/eamonn/time_series_data.
   Google Scholar
• Costa, Y. M. G., L. S. Oliveira, and C. N. Silla Jr. 2017. An evaluation of convolutional neural networks for music classification using spectrograms. Applied Soft Computing 52:28–38. doi:10.1016/j.asoc.2016.12.024.
   Web of Science ®Google Scholar
• Cui, Z., W. Chen, and Y. Chen. 2016. Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995.
   Google Scholar
• Delgado, M., M. P. Cuellar, and M. C. Pegalajar. 2008. Multiobjective hybrid optimization and training of recurrent neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38 (2):381–403. doi:10.1109/TSMCB.2007.912937.
   PubMed Web of Science ®Google Scholar
• Esling, P., and C. Agon. 2013. Multiobjective time series matching for audio classification and retrieval. IEEE Transactions on Audio, Speech, and Language Processing 21 (10):2057–72. doi:10.1109/TASL.2013.2265086.
   Google Scholar
• Geman, S., E. Bienenstock, and R. Doursat. 1992. Neural networks and the bias/variance dilemma. Neural Computation 4 (1):1–58. doi:10.1162/neco.1992.4.1.1.
   Web of Science ®Google Scholar
• Gurcan, M. N., H.-P. Chan, B. Sahiner, L. Hadjiiski, N. Petrick, and M. A. Helvie. 2002. Optimal neural network architecture selection: Improvement in computerized detection of microcalcifications. Academic Radiology 9 (4):420–29. doi:10.1016/S1076-6332(03)80187-3.
   PubMed Web of Science ®Google Scholar
• Ijjina, E. P., and C. K. Mohan. 2016. Hybrid deep neural network model for human action recognition. Applied Soft Computing 46:936–52. doi:10.1016/j.asoc.2015.08.025.
   Web of Science ®Google Scholar
• Jeong, Y.-S., M. K. Jeong, and O. A. Omitaomu. 2011. Weighted dynamic time warping for time series classification. Pattern Recognition 44 (9):2231–40. doi:10.1016/j.patcog.2010.09.022.
   Web of Science ®Google Scholar
• Kate, R. J. 2016. Using dynamic time warping distances as features for improved time series classification. Data Mining and Knowledge Discovery 30 (2):283–312. doi:10.1007/s10618-015-0418-x.
   Web of Science ®Google Scholar
• Keogh, E., and S. Kasetty. 2003. On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Mining and Knowledge Discovery 7 (4):349–71. doi:10.1023/A:1024988512476.
   Web of Science ®Google Scholar
• Kingma, D. P., and B. Jimmy 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Längkvist, M., L. Karlsson, and A. Loutfi. 2014. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters 42:11–24. doi:10.1016/j.patrec.2014.01.008.
   Web of Science ®Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature. 521 (7553):436. Google Scholar doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11):2278–324. doi:10.1109/5.726791.
   Web of Science ®Google Scholar
• LeCun, Y. A., L. Bottou, G. B. Orr, and M. Klaus-Robert. 2012. Efficient backprop. In Neural networks: Tricks of the trade, 9–48. Berlin, Heidelberg: Springer.
   Google Scholar
• Lewis, D. D. 1998. Naive (Bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning, Berlin, Heidelberg, 4–15. Springer.
   Google Scholar
• Li, H. 2015. On-line and dynamic time warping for time series data mining. International Journal of Machine Learning and Cybernetics 6 (1):145–53. doi:10.1007/s13042-014-0254-0.
   Web of Science ®Google Scholar
• Lin, J., E. Keogh, L. Wei, and S. Lonardi. 2007. Experiencing SAX: A novel symbolic representation of time series. Data Mining and Knowledge Discovery 15 (2):107–44. doi:10.1007/s10618-007-0064-z.
   Web of Science ®Google Scholar
• Lines, J., A. Bagnall, P. Caiger-Smith, and S. Anderson. 2011. Classification of household devices by electricity usage profiles. In International Conference on Intelligent Data Engineering and Automated Learning, Berlin, Heidelberg, 403–12. Springer.
   Google Scholar
• Prechelt, L. 2012. Early stoppingbut when?. In Neural networks: Tricks of the trade, 53–67. Berlin, Heidelberg: Springer.
   Google Scholar
• Rakthanmanon, T., B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh. 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data (TKDD) 7 (3):10. doi:10.1145/2513092.
   PubMed Web of Science ®Google Scholar
• Sak, H., A. Senior, and F. Beaufays. 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128.
   Google Scholar
• Schäfer, P. 2015. The BOSS is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery 29 (6):1505–30. doi:10.1007/s10618-014-0377-7.
   Web of Science ®Google Scholar
• Schäfer, P. 2016. Scalable time series classification. Data Mining and Knowledge Discovery 30 (5):1273–98. doi:10.1007/s10618-015-0441-y.
   Web of Science ®Google Scholar
• Schmidhuber, J. 2015. Deep learning in neural networks: An overview. Neural Networks 61:85–117. doi:10.1016/j.neunet.2014.09.003.
   PubMed Web of Science ®Google Scholar
• Sezer, O. B., and A. M. Ozbayoglu. 2018. Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach. Applied Soft Computing 70:525–38. doi:10.1016/j.asoc.2018.04.024.
   Web of Science ®Google Scholar
• Uktveris, T., and V. Jusas. 2017. Application of convolutional neural networks to four-class motor imagery classification problem. Information Technology And Control 46 (2):260–73. doi:10.5755/j01.itc.46.2.17528.
   Web of Science ®Google Scholar
• Wang, Z., W. Yan, and T. Oates. 2017. Time series classification from scratch with deep neural networks: A strong baseline. In Neural Networks (IJCNN), 2017 International Joint Conference on, 1578–85, Anchorage, Alaska, USA. IEEE.
   Google Scholar
• Weisstein, E. W. 1999. Convolution. Accessed November 30, 2018. http://mathworld.wolfram.com/Convolution.html.
   Google Scholar
• Wiens, J., E. Horvitz, and J. V. Guttag. 2012. Patient risk stratification for hospital-associated c. diff as a time-series classification task. In Advances in Neural Information Processing Systems, 467–75. Curran Associates Inc.
   Google Scholar
• Xing, Z., J. Pei, and E. Keogh. 2010. A brief survey on sequence classification. ACM Sigkdd Explorations Newsletter 12 (1):40–48. doi:10.1145/1882471.
   Google Scholar
• Zhao, B., L. Huanzhang, S. Chen, J. Liu, and W. Dongya. 2017. Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics 28 (1):162–69. doi:10.21629/JSEE.2017.01.18.
   Web of Science ®Google Scholar
• Zheng, Y., Q. Liu, E. Chen, G. Yong, and J. Leon Zhao. 2016. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Frontiers of Computer Science 10 (1):96–112. doi:10.1007/s11704-015-4478-2.
   Web of Science ®Google Scholar