Abstract
Most real-world sequence data for binary classification tasks appear to possess unilateral common factor. That is, samples from one of the classes occur because of common underlying causes while those from the other class may not. We are interested in resolving these tasks using convolutional neural networks (CNN). However, due to both the technical specification and the nature of the data, learning a classifier is generally associated with two problems: (1) defining a segmentation window size to sub-sequence for sufficient data augmentation and avoiding serious multiple-instance learning issue is non-trivial; (2) samples from one of the classes have common underlying causes and thus present similar features, while those from the other class can have various latent characteristics which can distract CNN in the learning process. We mitigate the first problem by introducing a random variable on sample scaling parameters, whose distribution’s parameters are jointly learnt with CNN and leads to what we call adaptive multi-scale sampling (AMS). To address the second problem, we propose activation pattern regularization (APR) on only samples with the common causes such that CNN focuses on learning representations pertaining to the common factor. We demonstrate the effectiveness of both proposals in extensive experiments on real-world datasets.
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Murray, J.F., Hughes, G.F., Kreutz-Delgado, K.: Machine learning methods for predicting failures in hard drives: a multiple-instance application. J. Mach. Learn. Res. 6, 783–816 (2005)
Liu, Y., Yao, K.T., Liu, S., Raghavendra, C.S., Balogun, O., Olabinjo, L.: Semi-supervised failure prediction for oil production wells. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 434–441, December 2011
Chalermarrewong, T., Achalakul, T., See, S.C.W.: Failure prediction of data centers using time series and fault tree analysis. In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems, pp. 794–799, December 2012
Steinwart, I., Hush, D., Scovel, C.: A classification framework for anomaly detection. J. Mach. Learn. Res. 6, 211–232 (2005)
Chitrakar, R., Chuanhe, H.: Anomaly detection using support vector machine classification with k-Medoids clustering. In: 2012 Third Asian Himalayas International Conference on Internet, pp. 1–5, November 2012
Bhattacharyya, D.K., Kalita, J.K.: Network Anomaly Detection: A Machine Learning Perspective. Chapman & Hall/CRC, Boca Raton (2013)
Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott Williams & Wilkins, Philadelphia (2004)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Wang, Z., Oates, T.: Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI (2015)
Zhiguang wang, T.O.: Imaging time-series to improve classification and imputation. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 3939–3945. AAAI (2015)
Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298–310. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08010-9_33
Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI 2015, pp. 3995–4001. AAAI Press (2015)
Tang, Y., Xu, J., Matsumoto, K., Ono, C.: Sequence-to-sequence model with attention for time series classification. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 503–510, December 2016
Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. SIGKDD Explor. Newsl. 12(1), 40–48 (2010)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, 1st edn. MIT Press, Cambridge (1998)
Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms - the numenta anomaly benchmark. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 38–44, December 2015
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Tang, Y., Yonekawa, K., Kurokawa, M., Wada, S., Yoshihara, K. (2018). Binary Classification of Sequences Possessing Unilateral Common Factor with AMS and APR. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_26
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DOI: https://doi.org/10.1007/978-3-319-93040-4_26
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