CSSI Elements: Development of Assumption-Free Parallel Data Curing Service for Robust Machine Learning and Statistical Predictions PI: In-Ho Cho, Co-PI: Jae-Kwang Kim Institutions: Iowa State University Award #: 1931380 NSF CSSI PI Meeting, Washington D.C., July 25-26, 2022 Grand Challenges Research Objective Proposed Methods Results User-Friendly Service Conclusions • Incomplete data issue is everywhere in broad science and engineering • Theories and methods of missing data curing (called “imputation”) is limited to small data • Naïve imputation may substantially hamper the accurate machine learning (ML) and statistical learning (SL)-based predictions (see Fig below) • Lack of theories and software for large/big incomplete data curing Fig. Positive impact of the proposed data curing method (FHDI) on statistical learning (SL) and ML predictions: Generalized additive model (GAM); Extremely randomized trees (ERT); Artificial neural network (ANN). Root mean square error (RMSE) is shown. • Develop a new community-level large data curing service running on NSF Cyberinfrastructure (XSEDE) and local HPC • No restriction of data sizes, types, high- dimensionality; No distributional assumptions or expert knowledge on data science required • Pursue a purely data-driven imputation by developing the ultra data-oriented parallel fractional hot deck imputation (UP-FHDI) ➤Assumption-Free, General Data Curing; Only Observed Data Are Needed for Imputation (thus, “Hot-deck”) ➤Provide “Cured” large/big data set for convenient subsequent ML and SL Ultra Data-Oriented Parallel Fractional Hot Deck Imputation (UP-FHDI) [Ultra Data: Big-n & Big-p] [Step 1] Parallel Imputation Cell Construction Hybrid Data (Continuous & Categorical) [Step 2] Imputation Cell’s Joint Probability Parallelized Modified EM Algorithm [Step 3] Fractional Hot Deck Imputation Parallelized Donor Selection with KNN [Step 4] Parallel Variance Estimation Parallelized Jackknife & Parallel Linearization [Step 0] Sure Independence Screening (SIS) Selectively Done for big-p (high-dimensional) Data Data: U(size, variables, missing rate) Fig. Scalability of UP-FHDI • Deployment of the UP-FHDI on NSF XSEDE • Graphical User Interface (GUI) for UP-FHDI • Open data sets, examples, and manual videos big-n data big-p data big-n & big-p data • UP-FHDI has been developed for improving prediction accuracy of ML and SL with Ultra incomplete data (up to millions of instances and 10,000 variables) • The program is deployable on NSF XSEDE and local HPC • Serial version R Package FHDI available on CRAN Acknowledgement • PIs work supported by NSF OAC-1931380 • HPC at Iowa State University: Condo supported by NSF MRI grant CNS-1229081 and CRI-1205413; Nova by MRI-1726447 and 2018594 ULTRA DATA Fig. UP-FHDI Can Tackle Three Data Types References of the PIs • Yang et al., 2022, IEEE TKDE (under 2nd review) • Yang et al., 2020, IEEE TKDE • Song et al., 2019, IEEE TKDE • Im et al., 2018, The R Journal Adopted OOOPS (Huang and Liu, 2020) Fig. Average Standard Error Fig. RMSE Synthetic Big-p Data (n=10,000, p=0.1 million, Missing=0.3) with increasing randomness (SD) in data Mean Abs Error GUI-App for UP-FHDI Fig. Accuracy of UP-FHDI Fig. New Variance Est. Method’s Efficiency Test Data Sets