NARMAX Model as a Sparse, Interpretable and Transparent Machine Learning Approach for Big Medical and Healthcare Data Analysis
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/8831117/8855328/08855492.pdf?arnumber=8855492
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting;2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2023-07-19
2. Efficient Mask Attention-Based NARMAX (MAB-NARMAX) Model Identification;2022 27th International Conference on Automation and Computing (ICAC);2022-09-01
3. The application of interpretable machine learning model based on comparative learning and NARMAX in epidemic research;2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA);2022-06-24
4. Tomato Disease Recognition Using a Compact Convolutional Neural Network;IEEE Access;2022
5. Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives;2021-11-01
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