CSSI Slide Cho and Kim (Iowa State Univ).pdf (764.88 kB)
CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions
Version 2 2020-02-03, 22:14
Version 1 2020-02-03, 22:12
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posted on 2020-02-03, 22:14 authored by In Ho ChoIn Ho Cho, Jae-Kwang KimDevelop 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.