  Feature transformation for AI is an essential task to boost the effectiveness
and interpretability of machine learning (ML). Feature transformation aims to
transform original data to identify an optimal feature space that enhances the
performances of a downstream ML model. Existing studies either combines
preprocessing, feature selection, and generation skills to empirically
transform data, or automate feature transformation by machine intelligence,
such as reinforcement learning. However, existing studies suffer from: 1)
high-dimensional non-discriminative feature space; 2) inability to represent
complex situational states; 3) inefficiency in integrating local and global
feature information. To fill the research gap, we formulate the feature
transformation task as an iterative, nested process of feature generation and
selection, where feature generation is to generate and add new features based
on original features, and feature selection is to remove redundant features to
control the size of feature space. Finally, we present extensive experiments
and case studies to illustrate 24.7\% improvements in F1 scores compared with
SOTAs and robustness in high-dimensional data.
