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High-Dimensional Limited-Sample Biomedical Data Classification Using Variational Autoencoder

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Data Mining (AusDM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

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Abstract

Biomedical prediction is vital to the modern scientific view of life, but it is a challenging task due to high-dimensionality, limited-sample size (also known as HDLSS problem), non-linearity, and data types tend are complex. A large number of dimensionality reduction techniques developed, but, unfortunately, not efficient with small-sample (observation) size dataset. To overcome the pitfalls of the sample-size and dimensionality this study employed variational autoencoder (VAE), which is a powerful framework for unsupervised learning in recent years. The aim of this study is to investigate a reliable biomedical diagnosis method for HDLSS dataset with minimal error. Hence, to evaluate the strength of the proposed model six genomic microarray datasets from Kent Ridge Repository were applied. In the experiment, several choices of dimensions were selected for data preprocessing. Moreover, to find a stable and suitable classifier, different popular classifiers were applied. The experimental results found that the VAE can provide superior performance compared to the traditional methods such as PCA, fastICA, FA, NMF, and LDA.

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Acknowledgments

This research is funded in part by the National Natural Science Foundations of China (Grant No. 61472258 and 61473194) and the Shenzhen-Hong Kong Technology Cooperation Foundation (Grant No. SGLH20161209101100926).

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Authors and Affiliations

  1. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Mohammad Sultan Mahmud, Xianghua Fu, Joshua Zhexue Huang & Md. Abdul Masud

Authors
  1. Mohammad Sultan Mahmud
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  2. Xianghua Fu
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  3. Joshua Zhexue Huang
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  4. Md. Abdul Masud
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Corresponding author

Correspondence to Mohammad Sultan Mahmud .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

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Mahmud, M.S., Fu, X., Huang, J.Z., Masud, M.A. (2019). High-Dimensional Limited-Sample Biomedical Data Classification Using Variational Autoencoder. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_3

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  • DOI: https://doi.org/10.1007/978-981-13-6661-1_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

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