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Feature transformation for AI is an essential task to boost the effectivenessand interpretability of machine learning (ML). Feature transformation aims totransform original data to identify an optimal feature space that enhances theperformances of a downstream ML model. Existing studies either combinespreprocessing, feature selection, and generation skills to empiricallytransform 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 representcomplex situational states; 3) inefficiency in integrating local and globalfeature information. To fill the research gap, we formulate the featuretransformation task as an iterative, nested process of feature generation andselection, where feature generation is to generate and add new features basedon original features, and feature selection is to remove redundant features tocontrol the size of feature space. Finally, we present extensive experimentsand case studies to illustrate 24.7\% improvements in F1 scores compared withSOTAs and robustness in high-dimensional data.

Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents