Mixed-Type Imputation for Missing Data Credal Classification via Quality Matrices
Author:
Affiliation:
1. School of Automation, Northwestern Polytechnical University, Xi’an, China
2. School of Electrical Engineering, Zhengzhou University, Luoyang, China
3. Univ. Rennes, CNRS, IRISA, DRUID, Lannion, France
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6221021/10604660/10517719.pdf?arnumber=10517719
Reference53 articles.
1. Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder
2. A Survey on Incomplete Multiview Clustering
3. A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data
4. Cost-Sensitive Active Learning for Incomplete Data
5. Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning
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