Feature transformation aims to generate new pattern-discriminative feature
space from original features to improve downstream machine learning (ML) task
performances. However, the discrete search space for the optimal feature
explosively grows on the basis of combinations of features and operations from
low-order forms to high-order forms. Existing methods, such as exhaustive
search, expansion reduction, evolutionary algorithms, reinforcement learning,
and iterative greedy, suffer from large search space. Overly emphasizing
efficiency in algorithm design usually sacrifices stability or robustness. To
fundamentally fill this gap, we reformulate discrete feature transformation as
a continuous space optimization task and develop an
embedding-optimization-reconstruction framework. This framework includes four
steps: 1) reinforcement-enhanced data preparation, aiming to prepare
high-quality transformation-accuracy training data; 2) feature transformation
operation sequence embedding, intending to encapsulate the knowledge of
prepared training data within a continuous space; 3) gradient-steered optimal
embedding search, dedicating to uncover potentially superior embeddings within
the learned space; 4) transformation operation sequence reconstruction,
striving to reproduce the feature transformation solution to pinpoint the
optimal feature space.

特征转换是从原始特征中生成新的模式区分性特征空间，以提高下游机器学习任务的性能。本文通过将离散特征转换重新定义为连续空间优化任务，并开发了一个嵌入 - 优化 - 重构的框架，以填补现有方法在大搜索空间下搜索效率和稳定性之间的缺陷。