Convolutional Neural Network for Time Series Cattle Behaviour Classification

ABSTRACT

An experiment is introduced which demonstrates the application of supervised feature learning using a Convolutional Neural Network for cattle behaviour classification. The data set used contains observations from sensors attached to the cattle. Previously this problem was addressed by classifying features learned by a stacked autoencoder. Here we explore an alternative method for learning effective features. Convolutional Neural Networks have shown immense success in computer vision, natural language processing, speech recognition etc. The success of Convolutional Neural Network in so many applications has inspired us to verify how effective this network is on learning features from cattle data. A shallow Convolutional Neural Network such as we have developed in this experiment learns effective features for classification and is time-efficient compared to previous model.

References

1. F. Chollet. keras. https://github.com/fchollet/keras, 2015.
2. W. Jiang and Z. Yin. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia, pages 1307--1310. ACM, 2015.
3. S. H. Khan, M. Bennamoun, F. Sohel, and R. Togneri. Cost sensitive learning of deep feature representations from imbalanced data. arXiv preprint arXiv:1508.03422, 2015.
4. Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller. Efficient backprop. In Neural networks: Tricks of the trade, pages 9--48. Springer, 2012.
5. Y. Lu, H. Guo, and L. Feldkamp. Robust neural learning from unbalanced data samples. In Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, volume 3, pages 1816--1821. IEEE, 1998.
6. Y. L. Murphey, H. Guo, and L. A. Feldkamp. Neural learning from unbalanced data. Applied Intelligence, 21(2):117--128, 2004.
7. A. Rahman, D. Smith, J. Hills, G. Bishop-Hurley, D. Henry, and R. Rawnsley. A comparison of autoencoder and statistical features for cattle behaviour classification. In IEEE World Congress on Computational Intelligence (IEEE WCCI) 2016, pages 1--7, 2016.
8. E. Ramentol, Y. Caballero, R. Bello, and F. Herrera. Smote-rsb*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowledge and information systems, 33(2):245--265, 2012.
9. D. Smith, R. Dutta, A. Hellicar, G. Bishop-Hurley, R. Rawnsley, D. Henry, J. Hills, and G. Timms. Bag of class posteriors, a new multivariate time series classifier applied to animal behaviour identification. Expert Systems with Applications, 42(7):3774--3784, 2015.
10. J. B. Yang, M. N. Nguyen, P. P. San, X. L. Li, and S. Krishnaswamy. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI'15, pages 3995--4001. AAAI Press, 2015.
11. M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang. Convolutional neural networks for human activity recognition using mobile sensors. In Mobile Computing, Applications and Services (MobiCASE), 2014 6th International Conference on, pages 197--205, Nov 2014.

• Computing methodologies

  • Machine learning

    • Machine learning approaches