CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions
ChoIn Ho
KimJae-Kwang
2020
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.