Cho, In Ho
Kim, Jae-Kwang
CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions
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.
NSF-CSSI-2020-Talk;Missing Data Imputation;Machine Learning;Statistical Inference;Parallel Computing
2020-02-03
https://figshare.com/articles/Untitled_Item/11796417

10.6084/m9.figshare.11796417.v2