In this paper, we study the problem of balancing effectiveness and efficiency
in automated feature selection. Feature selection is a fundamental intelligence
for machine learning and predictive analysis. After exploring many feature
selection methods, we observe a computational dilemma: 1) traditional feature
selection methods (e.g., mRMR) are mostly efficient, but difficult to identify
the best subset; 2) the emerging reinforced feature selection methods
automatically navigate feature space to explore the best subset, but are
usually inefficient. Are automation and efficiency always apart from each
other? Can we bridge the gap between effectiveness and efficiency under
automation? Motivated by such a computational dilemma, this study is to develop
a novel feature space navigation method. To that end, we propose an Interactive
Reinforced Feature Selection (IRFS) framework that guides agents by not just
self-exploration experience, but also diverse external skilled trainers to
accelerate learning for feature exploration. Specifically, we formulate the
feature selection problem into an interactive reinforcement learning framework.
In this framework, we first model two trainers skilled at different searching
strategies: (1) KBest based trainer; (2) Decision Tree based trainer. We then
develop two strategies: (1) to identify assertive and hesitant agents to
diversify agent training, and (2) to enable the two trainers to take the
teaching role in different stages to fuse the experiences of the trainers and
diversify teaching process. Such a hybrid teaching strategy can help agents to
learn broader knowledge, and, thereafter, be more effective. Finally, we
present extensive experiments on real-world datasets to demonstrate the
improved performances of our method: more efficient than existing reinforced
selection and more effective than classic selection.

研究如何在自动化特征选择中平衡有效性和效率。通过提出一个新的交互式增强特征选择框架，将特征选择问题形式化为交互式强化学习框架，该框架通过自我探索经验及多元化外部技术指导训练器，从而加速学习。此外，该研究利用多种不同搜索策略进行训练，并提出了一种混合教学策略来帮助智能体学习更广泛的知识。最后，对真实世界数据集进行广泛实验以展示该方法的性能改进。