Cho, In Ho
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. <br>
NSF-CSSI-2020-Poster;missing data imputation;machine Learning;Statistical Learning;Parallel calculations
2020-02-03
https://figshare.com/articles/CSSI_Elements_Development_of_Assumption-Free_Parallel_Data_Curing_Service_for_Robust_Machine_Learning_and_Statistical_Predictions/11796390

10.6084/m9.figshare.11796390.v1