Missing data imputation using statistical and machine learning methods in a real breast cancer problem

By José Manuel Jerez, Ignacio Molina, Pedro J. García-Laencina, Emilio Alba, Nuria Ribelles, Miguel Martín, Leonardo Franco. Artif. Intell. Medicine, 2010

582 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f77854f94c2f849ed93c018662bb3f0a3f8e38d2

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1. Yuchen Jiang
2. Okyay Kaynak
3. Shen Yin
4. Jingwei Dong
5. Rui Alexandre de Matos Araújo
6. Francisco Alexandre A. Souza
7. Mphago Banyatsang
8. Tlamelo Emmanuel

Citations in the corpus

• Review of soft sensor methods for regression applications (2016, methodology): “linear relationships, while in [16] several nonlinear models were used to model the relationship among the non-missing”
• A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes (2021, methodology): “Two other approaches employed to deal with missing data are expectation maximization [21] and maximum likelihood [22], which assume the missing data are subject to a cer-”
• A survey on missing data in machine learning (2021, methodology): “In most studies simple imputation methods are used because of their simplicity and that they can be used as an easy reference technique [38].”