CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions
In Ho Cho
Jae-Kwang Kim
10.6084/m9.figshare.11796417.v2
https://figshare.com/articles/Untitled_Item/11796417
Develop an assumption-free, general-purpose missing data curing service
on parallel computing environment for robust machine learning and
statistical inference using large/big incomplete data in broad science
and engineering. Parallel Fractional Hot Deck Imputation (P-FHDI) is
being developed in conjunction with hybrid parallelisms and sure
independence screening technique. Results demonstrate a promising
performance of the P-FHDI in improving subsequent machine learning and
statistical prediction with large incomplete data with favorable
scalability.
2020-02-03 22:14:24
NSF-CSSI-2020-Talk
Missing Data Imputation
Machine Learning
Statistical Inference
Parallel Computing