Feature selection aims to select a subset of features to optimize the downstream predictive tasks. MARFS has been introduced to automate feature selection, by creating agents for each feature. We develop a single-agent reinforced feature selection approach integrated with a restructured choice strategy.
Feature selection aims to select a subset of features to optimize the
performances of downstream predictive tasks. Recently, multi-agent reinforced
feature selection (MARFS) has been introduced to automate feature selection, by
creating agents for each feature to select or deselect corresponding features.
Although MARFS enjoys the automation of the selection process, MARFS suffers
from not just the data complexity in terms of contents and dimensionality, but
also the exponentially-increasing computational costs with regard to the number
of agents. The raised concern leads to a new research question: Can we simplify
the selection process of agents under reinforcement learning context so as to
improve the efficiency and costs of feature selection? To address the question,
we develop a single-agent reinforced feature selection approach integrated with
restructured choice strategy. Specifically, the restructured choice strategy
includes: 1) we exploit only one single agent to handle the selection task of
multiple features, instead of using multiple agents. 2) we develop a scanning
method to empower the single agent to make multiple selection/deselection
decisions in each round of scanning. 3) we exploit the relevance to predictive
labels of features to prioritize the scanning orders of the agent for multiple
features. 4) we propose a convolutional auto-encoder algorithm, integrated with
the encoded index information of features, to improve state representation. 5)
we design a reward scheme that take into account both prediction accuracy and
feature redundancy to facilitate the exploration process. Finally, we present
extensive experimental results to demonstrate the efficiency and effectiveness
of the proposed method.