تقرير
Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
| العنوان: | Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning |
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| المؤلفون: | Liu, Jing-Jing, Yao, Jie-Peng, Liu, Jin-Hang, Wang, Zhong-Yi, Huang, Lan |
| بيانات النشر: | SPRINGER |
| سنة النشر: | 2024 |
| المجموعة: | Institute of Automation: CASIA OpenIR (Chinese Academy of Sciences) / 中国科学院自动化研究所机构知识库 |
| مصطلحات موضوعية: | Missing time series data, Small samples, Imputation, Classification, Gradient penalized adversarial Multitasking, FAULT-DIAGNOSIS, NETWORKS, IMPACT, Computer Science, Artificial Intelligence |
| الوصف: | In practice, time series data obtained is usually small and missing, which poses a great challenge to data analysis in different domains, such as increasing the bias of model predictions, reducing the accuracy of model classification, and affecting the analysis data. This paper aims to address the problem of missing data imputation and classification of small sample time series data. By exploring and implementing efficient data interpolation strategies to improve classification accuracy, the robustness and accuracy of classification models in the face of incomplete data. To achieve this, we propose a new model that can effectively classify time series data with missing values. Our model utilizes a bi-directional long short-term memory network combined with an extreme learning machine for the imputation task, which can recover the missing time series values. For the classification task, we employ a self-attentional Inception Time network, which is regularized by a classification loss to effectively mitigate network overfitting. To improve the performance of the model on small sample time series datasets, we use a gradient penalty adversarial training approach. Our model integrates the advantages of multiple network modules, the gradient penalty adversarial multi-task model achieves optimal imputation and robust classification of missing small sample time series data. To evaluate the overall performance of our model, we selected forty datasets from the UCR time series datasets, and selected the German emotional speech datasets and the EEG epilepsy datasets, with the plant electrical signal datasets obtained from real measurements. A series of experiments were conducted to evaluate the effectiveness of our method compared to other methods, the datasets were set up with multiple missing rates, with root mean square error and coefficient of determination to assess the accuracy of imputation, and with accuracy to assess the performance of the classification task. The results show that our proposed method outperforms ... |
| نوع الوثيقة: | report |
| اللغة: | English |
| العلاقة: | APPLIED INTELLIGENCE; http://ir.ia.ac.cn/handle/173211/55620Test |
| DOI: | 10.1007/s10489-024-05314-3 |
| الإتاحة: | https://doi.org/10.1007/s10489-024-05314-3Test http://ir.ia.ac.cn/handle/173211/55620Test |
| رقم الانضمام: | edsbas.6DD405E5 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s10489-024-05314-3 |
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