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Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence
| Content Provider | MDPI |
|---|---|
| Author | Zhou, Yuxi Hong, Shenda Shang, Junyuan Wu, Meng Wang, Qingyun Li, Hongyan Xie, Junqing |
| Copyright Year | 2020 |
| Description | Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor network technology. Recently, deep-learning-based methods have achieved initial success in health-condition assessment research, but insufficient considerations for problems such as class skewness, noisy segments, and result interpretability make it difficult to apply them to real-world applications. In this paper, we propose a K-margin-based Interpretable Learning approach for health-condition assessment. In detail, a skewness-aware RCR-Net model is employed to handle problems of class skewness. Furthermore, we present a diagnosis model based on K-margin to automatically handle noisy segments by naturally exploiting expected consistency among the segments associated with each record. Additionally, a knowledge-directed interpretation method is presented to learn domain knowledge-level features automatically without the help of human experts which can be used as an interpretable decision-making basis. Finally, through experimental validation in the field of both medical and aerospace, the proposed method has a better generality and high efficiency with 0.7974 and 0.8005 |
| Starting Page | 7307 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20247307 |
| Journal | Sensors |
| Issue Number | 24 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-19 |
| Access Restriction | Open |
| Subject Keyword | Sensors Artificial Intelligence Industrial Engineering Noise Class Skewness Model Interpretability Deep Learning Health-condition Assessment |
| Content Type | Text |
| Resource Type | Article |
