Feature selection aims to identify the most pattern-discriminative feature subset. In prior
literature, filter (eg, backward elimination) and embedded (eg, LASSO) methods have …

Tabular data is one of the most widely used formats across industries, driving critical
applications in areas such as finance, healthcare, and marketing. In the era of data-centric …

Polymer property performance prediction aims to forecast specific features or attributes of
polymers, which has become an efficient ap-proach to measuring their performance …

Feature selection aims to identify the optimal feature subset for enhancing downstream
models. Effective feature selection can remove redundant features, save computational …

Feature space is an environment where data points are vectorized to represent the original
dataset. Reconstructing a good feature space is essential to augment the AI power of data …

Data-Centric AI (DCAI) aims to use AI to get better data for better AI. Feature transformation,
as one of the essential tasks of DCAI, can augment the data representation and has …

This thesis addresses the challenge of nonlinear feature selection in datasets that include
categorical features. Conventional feature selection methods often struggle with nonlinear …

This thesis addresses the challenge of nonlinear feature selection in datasets that include
categorical features. Conventional feature selection methods often struggle with nonlinear …