Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Generative design refers to a methodology that not only simulates the characteristics of a
given data or system but also creates artificial data for various purposes. It'sa significant …

Online learning with feature spaces that are not fixed but can vary over time renders a
seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two …

Purpose Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a complex and
high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores …

Missing data are present in most real world problems and need careful handling to preserve
the prediction accuracy and statistical consistency in the downstream analysis. As the gold …

Shapley values originated in cooperative game theory but are extensively used today as a
model-agnostic explanation framework to explain predictions made by complex machine …

Missing data is common in applied data science, particularly for tabular data sets found in
healthcare, social sciences, and natural sciences. Most supervised learning methods only …

In recent years, the diabetes population has grown younger. Therefore, it has become a key
problem to make a timely and effective prediction of diabetes, especially given a single data …

This paper explores a new online learning problem where the data streams are generated
from an over-time varying feature space, in which the random variables are of mixed data …

Social science approaches to missing values predict avoided, unrequested, or lost
information from dense data sets, typically surveys. The authors propose a matrix …