Abstract
Extreme Learning Machine (ELM for shot) is a single hidden layer feed-forward network, where the weights between input and hidden layer are initialized randomly. ELM is efficient due to its utilization of the analytical approach to compute weights between hidden and output layer. However, ELM still fails to output the semantic classification outcome. To address such limitation, in this paper, we propose a diversified top-k shapelets transform framework to improve representative and interpretative ability of ELM. Specifically, we first define the similar shapelets and diversified top-k shapelets to construct diversity shapelets graph. Then, a novel diversity graph based top-k shapelets extraction algorithm to search diversified top-k shapelets. Finally, we propose a shapelets transformed ELM algorithm named as DivShapELM to automatically determine the k value, which is further utilized for time series classification. The experimental results demonstrate that the proposed approach significantly outperforms traditional ELM algorithm in terms of effectiveness and efficiency.
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References
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the IJCNN2004, pp. 985–990 D(2004)
Zong, W., Huang, G.-B., Chen, Y.: Weighted extreme learning machine for imbalance learning. J. Neurocomputing 101, 229–242 (2013)
Savojardo, C., Fariselli, P., Casadio, R.: BETAWARE: a machine-learning tool to detect and predict transmembrane beta barrel proteins in prokaryotes. J. Bioinf. 29(4), 504–505 (2013)
Zhao, Y., Wang, G., Yin, Y., Li, Y., Wang, Z.: Improving ELM-based microarray data classification by diversified sequence features selection. J. Neural Comput. Applic. 27(1), 155–166 (2016)
Ye, L., Keogh, E.: Time Series Shapelets: A New Primitive for Data Mining. In: Proceedings of the 15th ACM SIGKD, pp. 947–956 (2009)
Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 13th SDM, pp. 668–676 (2013)
Lines, J., Davis, L.M., Hills, J., et al.: A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD, pp. 289–297 (2012)
Hills, J., Lines, J., Baranauskas, E., et al.: Classification of time series by shapelet transformation. J. Data Min. Knowl. Discovery 28(4), 851–881 (2014)
Zakaria, J., Mueen, A., Keogh, E.: Clustering time series using unsupervised-shapelets. In: Proceedings of the 12th ICDM, pp. 785–794 (2012)
Xing, Z., Pei, J., Philip, S.Y., et al.: Extracting interpretable features for early classification on time series. In: Proceedings of the 11th SDM, pp. 247–258 (2011)
Yuan, J.D., Wang, Z.H., Han, M.: Shapelet pruning and shapelet coverage for time series classification. J. Softw. 26(9), 2311–2325 (2015)
Qin, L., Yu, J.X., Chang, L.: Diversifying top-k results. Proc. VLDB Endowment 5(11), 1124–1135 (2012)
Wang, Y., Lin, X., Wu, L., et al.: Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans. Image Process. 24(11), 3939–3949 (2015)
Wang, Y., Zhang, W., Wu, L., Lin, X., Zhao, X.: Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion. IEEE Trans. Neural Netw. Learn. Syst. 1–14 (2015)
Wang, Y., Lin, X., Wu, L., Zhang, W.: Effective multi-query expansions: robust landmark retrieval. In: ACM Multimedia, pp. 79–88 (2015)
Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q.: LBMCH: learning bridging mapping for cross-modal hashing. In: ACM SIGIR, pp. 999–1002 (2015)
Wang, Y., Lin, X., Zhang, Q.: Towards metric fusion on multi-view data: a cross-view based graph random walk approach. In: ACM CIKM, pp. 805–810 (2013)
Wu, L., Wang, Y., Shepherd, J.: Efficient image and tag co-ranking: a bregman divergence optimization method. In: ACM Multimedia, pp. 593–596 (2013)
Wang, Y., Lin, X., Wu, L., Zhang, W., Zhang, Q.: Exploiting correlation consensus: towards subspace clustering for multi-modal data. In: ACM Multimedia, pp. 981–984 (2014)
Yang, W., Wenjie, Z., Lin, W., Xuemin, L., Meng, F., Shirui, P.: Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering. In: IJCAI, pp. 2153–2159 (2016)
Acknowledgments
Supported by the Youth Science Foundation of China University of Mining and Technology under Grant No. (2013QNB16), Natural Science Foundation of Jiangsu Province of China (BK20140192).
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Yan, Q., Sun, Q., Yan, X. (2016). Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_17
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DOI: https://doi.org/10.1007/978-3-319-46922-5_17
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