  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.
