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Adaptive Scaling for U-Net in Time Series Classification

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Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13623))

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Abstract

Convolutional Neural Networks such as U-Net are recently getting popular among researchers in many applications, such as Biomedical Image Segmentation. U-Net is one of the popular deep Convolutional Neural Networks which first contracts the input image using pooling layers and then upscales the feature maps before classifying them. In this paper, we explore the performance of adaptive scaling for U-Net in time series classification. Also, to improve performance, we extract features from the trained U-Net model and use ensemble deep Random Vector Functional Link (edRVFL) to classify them. Experiments on 55 large UCR datasets reveal that adaptive scaling improves the performance of U-Net in time series classification. Also, using edRVFL on extracted features from the trained U-Net model enhances performance. Consequently, our U-Net-edRVFL classifier outperforms other time series classification methods.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Wen Xin Cheng

  2. KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar

    Ponnuthurai Nagaratnam Suganthan

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  1. Wen Xin Cheng
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  2. Ponnuthurai Nagaratnam Suganthan
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Correspondence to Wen Xin Cheng .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Cheng, W.X., Suganthan, P.N. (2023). Adaptive Scaling for U-Net in Time Series Classification. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_26

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  • DOI: https://doi.org/10.1007/978-3-031-30105-6_26

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