A new approach to generating virtual samples to enhance classification accuracy with small data—a case of bladder cancer

Liang-Sian Lin; Susan C Hu; Yao-San Lin; Der-Chiang Li; Liang-Ren Siao; Liang-Sian Lin; Susan C Hu; Yao-San Lin; Der-Chiang Li; Liang-Ren Siao

doi:10.3934/mbe.2022290

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 6204-6233. doi: 10.3934/mbe.2022290

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A new approach to generating virtual samples to enhance classification accuracy with small data—a case of bladder cancer

1.
Department of Information Management, National Taipei University of Nursing and Health Sciences, Ming-te Road, Taipei 112303, Taiwan
2.
Department of Public Health, College of Medicine, National Cheng Kung University, University Road, Tainan 70101, Taiwan
3.
Singapore Centre for Chinese Language, Nanyang Technological University, Ghim Moh Road Singapore 279623, Singapore
4.
Department of Industrial and Information Management, National Cheng Kung University, University Road, Tainan 70101, Taiwan

Academic Editor: Nataliya Shakhovska

Received: 13 December 2021 Revised: 06 March 2022 Accepted: 28 March 2022 Published: 15 April 2022

In the medical field, researchers are often unable to obtain the sufficient samples in a short period of time necessary to build a stable data-driven forecasting model used to classify a new disease. To address the problem of small data learning, many studies have demonstrated that generating virtual samples intended to augment the amount of training data is an effective approach, as it helps to improve forecasting models with small datasets. One of the most popular methods used in these studies is the mega-trend-diffusion (MTD) technique, which is widely used in various fields. The effectiveness of the MTD technique depends on the degree of data diffusion. However, data diffusion is seriously affected by extreme values. In addition, the MTD method only considers data fitted using a unimodal triangular membership function. However, in fact, data may come from multiple distributions in the real world. Therefore, considering the fact that data comes from multi-distributions, in this paper, a distance-based mega-trend-diffusion (DB-MTD) technique is proposed to appropriately estimate the degree of data diffusion with less impacts from extreme values. In the proposed method, it is assumed that the data is fitted by the triangular and trapezoidal membership functions to generate virtual samples. In addition, a possibility evaluation mechanism is proposed to measure the applicability of the virtual samples. In our experiment, two bladder cancer datasets are used to verify the effectiveness of the proposed DB-MTD method. The experimental results demonstrated that the proposed method outperforms other VSG techniques in classification and regression items for small bladder cancer datasets.
- small data approach,
- virtual sample generation,
- data-driven model
Citation: Liang-Sian Lin, Susan C Hu, Yao-San Lin, Der-Chiang Li, Liang-Ren Siao. A new approach to generating virtual samples to enhance classification accuracy with small data—a case of bladder cancer[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 6204-6233. doi: 10.3934/mbe.2022290

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Abstract

In the medical field, researchers are often unable to obtain the sufficient samples in a short period of time necessary to build a stable data-driven forecasting model used to classify a new disease. To address the problem of small data learning, many studies have demonstrated that generating virtual samples intended to augment the amount of training data is an effective approach, as it helps to improve forecasting models with small datasets. One of the most popular methods used in these studies is the mega-trend-diffusion (MTD) technique, which is widely used in various fields. The effectiveness of the MTD technique depends on the degree of data diffusion. However, data diffusion is seriously affected by extreme values. In addition, the MTD method only considers data fitted using a unimodal triangular membership function. However, in fact, data may come from multiple distributions in the real world. Therefore, considering the fact that data comes from multi-distributions, in this paper, a distance-based mega-trend-diffusion (DB-MTD) technique is proposed to appropriately estimate the degree of data diffusion with less impacts from extreme values. In the proposed method, it is assumed that the data is fitted by the triangular and trapezoidal membership functions to generate virtual samples. In addition, a possibility evaluation mechanism is proposed to measure the applicability of the virtual samples. In our experiment, two bladder cancer datasets are used to verify the effectiveness of the proposed DB-MTD method. The experimental results demonstrated that the proposed method outperforms other VSG techniques in classification and regression items for small bladder cancer datasets.

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