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Conquering insufficient/imbalanced data learning for the Internet of Medical Things

  • S.I. : Neural Computing for IOT based Intelligent Healthcare Systems
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Abstract

This study presents a data augmentation technique that solves insufficient/imbalanced data problems during crowdsensing by the Internet of Medical Things (IoMT) or wireless sensor networks (WSNs), owing to diversified locations and heterogeneous conditions. This may cause problems because the samples in various categories may vary in quantities, which create skew distributions. Besides, pattern analysis of insufficient observed samples also generates biased models. In view of such, this work proposes synthetic minority oversampling generative adversarial networks (SMOGANs) for processing imbalanced data, where insufficient samples in quantities can be automatically expanded, so that different classes contain equal numbers of samples, subsequently avoiding biased modeling. The SMOGAN consists of two modules, where the first one is the synthetic minority oversampling technique (SMOTE), and the second involves a GAN. The former is used to initialize the proposed system, in which insufficient/imbalanced data samples are roughly augmented in quantities. Subsequently, the GAN enriches feature diversities of those pseudoreal samples formerly augmented by the SMOTE. Experiments on open datasets were carried out for evaluation. To assess the capability of data augmentation, only 4.00% of the real data were reserved as minority classes and then sent into different data augmentation methods for comparison. Analytical results showed that the proposed SMOGANs outperformed the baselines. Accuracy was increased compared with the baselines. Such results showed that the proposed SMOGAN could improve data collection problems of insufficient/imbalanced datasets by enhancing data quantities and qualities.

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Acknowledgements

This work is supported in part by the Ministry of Science and Technology, Taiwan (107-2218-E-110-013-MY3) and by 2019 NVIDIA Data Science GPU Grants (Project: SMO-GANs for Extremely Imbalanced Data).

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Authors and Affiliations

  1. National Sun Yat-Sen University, Kaohsiung, Taiwan

    Zi-Ching Lan, Guan-Yu Huang, Yun-Pei Li & Bo-Wei Chen

  2. Chung-Ang University, Seoul, Korea

    Seungmin Rho

  3. Ramco Institute of Technology, Rajapalayam, India

    S. Vimal

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  1. Zi-Ching Lan
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  2. Guan-Yu Huang
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  3. Yun-Pei Li
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Correspondence to Bo-Wei Chen.

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Lan, ZC., Huang, GY., Li, YP. et al. Conquering insufficient/imbalanced data learning for the Internet of Medical Things. Neural Comput & Applic 35, 22949–22958 (2023). https://doi.org/10.1007/s00521-022-06897-z

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