  In this paper, we propose a single-agent Monte Carlo based reinforced feature
selection (MCRFS) method, as well as two efficiency improvement strategies,
i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy.
Feature selection is one of the most important technologies in data
prepossessing, aiming to find the optimal feature subset for a given downstream
machine learning task. Enormous research has been done to improve its
effectiveness and efficiency. Recently, the multi-agent reinforced feature
selection (MARFS) has achieved great success in improving the performance of
feature selection. However, MARFS suffers from the heavy burden of
computational cost, which greatly limits its application in real-world
scenarios. In this paper, we propose an efficient reinforcement feature
selection method, which uses one agent to traverse the whole feature set, and
decides to select or not select each feature one by one. Specifically, we first
develop one behavior policy and use it to traverse the feature set and generate
training data. And then, we evaluate the target policy based on the training
data and improve the target policy by Bellman equation. Besides, we conduct the
importance sampling in an incremental way, and propose an early stopping
strategy to improve the training efficiency by the removal of skew data. In the
early stopping strategy, the behavior policy stops traversing with a
probability inversely proportional to the importance sampling weight. In
addition, we propose a reward-level interactive strategy to improve the
training efficiency via reward-level external advice. Finally, we design
extensive experiments on real-world data to demonstrate the superiority of the
proposed method.

Machine Learning