Feature selection can be a crucial factor in obtaining robust and accurate
predictions. Online feature selection models, however, operate under
considerable restrictions; they need to efficiently extract salient input
features based on a bounded set of observations, while enabling robust and
accurate predictions. In this work, we introduce FIRES, a novel framework for
online feature selection. The proposed feature weighting mechanism leverages
the importance information inherent in the parameters of a predictive model. By
treating model parameters as random variables, we can penalize features with
high uncertainty and thus generate more stable feature sets. Our framework is
generic in that it leaves the choice of the underlying model to the user.
Strikingly, experiments suggest that the model complexity has only a minor
effect on the discriminative power and stability of the selected feature sets.
In fact, using a simple linear model, FIRES obtains feature sets that compete
with state-of-the-art methods, while dramatically reducing computation time. In
addition, experiments show that the proposed framework is clearly superior in
terms of feature selection stability.

本文提出了一种基于 FIRES 的在线特征选择新框架，通过处理模型参数作为随机变量惩罚不确定性高的特征，选择更加稳定的特征集，并实验证明该框架在稳定性方面显著优于现有方法。