Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
This work addresses the need for efficient and stable feature selection in online learning scenarios, offering a generic solution that is incremental in its approach.
The authors tackled the problem of online feature selection under computational constraints by introducing FIRES, a framework that uses model parameter uncertainty to penalize features, resulting in competitive accuracy and stability with significantly reduced computation time compared to state-of-the-art methods.
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.