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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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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