%0 DATA
%A In Ho, Cho
%A Jae-Kwang, Kim
%D 2020
%T CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions
%U https://figshare.com/articles/Untitled_Item/11796417
%R 10.6084/m9.figshare.11796417.v2
%2 https://ndownloader.figshare.com/files/21528108
%K NSF-CSSI-2020-Talk
%K Missing Data Imputation
%K Machine Learning
%K Statistical Inference
%K Parallel Computing
%X 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.