Scholarlink

<p>In recent years, industrial context deep learning algorithms have been widely applied to support decision-makers. However, in early-stage decision support systems, due to limited data collection periods and demanding experimental conditions, decision-makers often face the dilemma of data scarcity and insufficient feature representation. Moreover, with increasing complexity in industrial processes, it becomes more challenging to extract representative information from collected data exhibiting high dimensionality and non-linear characteristics. As a result, achieving strong predictive performance is difficult with conventional deep learning algorithms. To tackle this issue, we propose a novel virtual sample generation (VSG) technique, termed Bézier-Unet-GAN, an auto-encoder U-net generative adversarial network (GAN) model designed to enhance prediction accuracy on small-sample industrial data. The proposed method aims to produce high-quality virtual samples resembling original data. Additionally, we employ a manifold algorithm to distill representative features from the original data. Based on the features, we create Bézier interpolation point and calculate its membership function (MF) to train a bagging-based U-net generator. To evaluate efficacy of the proposed approach, we conduct experiments on six industrial datasets. Compared with eight state-of-the-art VSG techniques, the proposed approach achieves promising results in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) evaluation metrics. For example, at the sample size of 10, for a back propagation neural network (BPNN) predictive model, the proposed method improves the metrics by 11.928%, 19.067%, and 10.442%, respectively. Additionally, we use the Siegel-Castellan post hoc test and Wilcoxon test to elucidate whether prediction accuracy using the Bézier-Unet-GAN method significantly outperforms the other eight VSG techniques across MAE, MAPE, and RMSE metrics. The experimental results indicate that the Bézier-Unet-GAN method effectively improves the performance of the predictive model on small sample sets while consistently outperforming these state-of-the-art VSG techniques. Overall, the proposed approach delivers superior predictive performance on limited industrial datasets relative to the compared methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing small data prediction performance via Bézier-interpolated virtual sample generation with bagging U-net GAN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

查看全文
  • Liang-Sian Lin,
  • Hui-Chi Chuang,
  • Yi-Chung Cheng,
  • Chih-Chuan Chen,
  • Che-Jung Chang

摘要

In recent years, industrial context deep learning algorithms have been widely applied to support decision-makers. However, in early-stage decision support systems, due to limited data collection periods and demanding experimental conditions, decision-makers often face the dilemma of data scarcity and insufficient feature representation. Moreover, with increasing complexity in industrial processes, it becomes more challenging to extract representative information from collected data exhibiting high dimensionality and non-linear characteristics. As a result, achieving strong predictive performance is difficult with conventional deep learning algorithms. To tackle this issue, we propose a novel virtual sample generation (VSG) technique, termed Bézier-Unet-GAN, an auto-encoder U-net generative adversarial network (GAN) model designed to enhance prediction accuracy on small-sample industrial data. The proposed method aims to produce high-quality virtual samples resembling original data. Additionally, we employ a manifold algorithm to distill representative features from the original data. Based on the features, we create Bézier interpolation point and calculate its membership function (MF) to train a bagging-based U-net generator. To evaluate efficacy of the proposed approach, we conduct experiments on six industrial datasets. Compared with eight state-of-the-art VSG techniques, the proposed approach achieves promising results in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) evaluation metrics. For example, at the sample size of 10, for a back propagation neural network (BPNN) predictive model, the proposed method improves the metrics by 11.928%, 19.067%, and 10.442%, respectively. Additionally, we use the Siegel-Castellan post hoc test and Wilcoxon test to elucidate whether prediction accuracy using the Bézier-Unet-GAN method significantly outperforms the other eight VSG techniques across MAE, MAPE, and RMSE metrics. The experimental results indicate that the Bézier-Unet-GAN method effectively improves the performance of the predictive model on small sample sets while consistently outperforming these state-of-the-art VSG techniques. Overall, the proposed approach delivers superior predictive performance on limited industrial datasets relative to the compared methods.