  The goal of Feature Selection - comprising filter, wrapper, and embedded
approaches - is to find the optimal feature subset for designated downstream
tasks. Nevertheless, current feature selection methods are limited by: 1) the
selection criteria of these methods are varied for different domains, making
them hard to generalize; 2) the selection performance of these approaches drops
significantly when processing high-dimensional feature space coupled with small
sample size. In light of these challenges, we pose the question: can selected
feature subsets be more robust, accurate, and input dimensionality agnostic? In
this paper, we reformulate the feature selection problem as a deep
differentiable optimization task and propose a new research perspective:
conceptualizing discrete feature subsetting as continuous embedding space
optimization. We introduce a novel and principled framework that encompasses a
sequential encoder, an accuracy evaluator, a sequential decoder, and a gradient
ascent optimizer. This comprehensive framework includes four important steps:
preparation of features-accuracy training data, deep feature subset embedding,
gradient-optimized search, and feature subset reconstruction. Specifically, we
utilize reinforcement feature selection learning to generate diverse and
high-quality training data and enhance generalization. By optimizing
reconstruction and accuracy losses, we embed feature selection knowledge into a
continuous space using an encoder-evaluator-decoder model structure. We employ
a gradient ascent search algorithm to find better embeddings in the learned
embedding space. Furthermore, we reconstruct feature selection solutions using
these embeddings and select the feature subset with the highest performance for
downstream tasks as the optimal subset.
