  Representation (feature) space is an environment where data points are
vectorized, distances are computed, patterns are characterized, and geometric
structures are embedded. Extracting a good representation space is critical to
address the curse of dimensionality, improve model generalization, overcome
data sparsity, and increase the availability of classic models. Existing
literature, such as feature engineering and representation learning, is limited
in achieving full automation (e.g., over heavy reliance on intensive labor and
empirical experiences), explainable explicitness (e.g., traceable
reconstruction process and explainable new features), and flexible optimal
(e.g., optimal feature space reconstruction is not embedded into downstream
tasks). Can we simultaneously address the automation, explicitness, and optimal
challenges in representation space reconstruction for a machine learning task?
To answer this question, we propose a group-wise reinforcement generation
perspective. We reformulate representation space reconstruction into an
interactive process of nested feature generation and selection, where feature
generation is to generate new meaningful and explicit features, and feature
selection is to eliminate redundant features to control feature sizes. We
develop a cascading reinforcement learning method that leverages three
cascading Markov Decision Processes to learn optimal generation policies to
automate the selection of features and operations and the feature crossing. We
design a group-wise generation strategy to cross a feature group, an operation,
and another feature group to generate new features and find the strategy that
can enhance exploration efficiency and augment reward signals of cascading
agents. Finally, we present extensive experiments to demonstrate the
effectiveness, efficiency, traceability, and explicitness of our system.
