1. Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders
- Author
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Kristian Miok, Dong Nguyen-Doan, Marko Robnik-Šikonja, and Daniela Zaharie
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Monte Carlo method ,Machine Learning (stat.ML) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Machine Learning (cs.LG) ,missing data imputation ,Biomedical data ,Statistics - Machine Learning ,data preprocessing ,Missing data imputation ,050602 political science & public administration ,0202 electrical engineering, electronic engineering, information engineering ,Imputation (statistics) ,Monte Carlo dropout ,business.industry ,05 social sciences ,Missing data ,0506 political science ,deep learning models ,Deep neural networks ,020201 artificial intelligence & image processing ,Artificial intelligence ,Data pre-processing ,business ,computer - Abstract
Due to complex experimental settings, missing values are common in biomedical data. To handle this issue, many methods have been proposed, from ignoring incomplete instances to various data imputation approaches. With the recent rise of deep neural networks, the field of missing data imputation has oriented towards modelling of the data distribution. This paper presents an approach based on Monte Carlo dropout within (Variational) Autoencoders which offers not only very good adaptation to the distribution of the data but also allows generation of new data, adapted to each specific instance. The evaluation shows that the imputation error and predictive similarity can be improved with the proposed approach.
- Published
- 2019
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