Leveraging Model Inherent Variable Importance for Stable Online Feature Selection

TL;DR

FIRES is a new online feature selection framework that uses model parameter uncertainty to select stable, salient features efficiently, regardless of the underlying model complexity.

Contribution

Introduces FIRES, a generic framework leveraging model parameter importance for stable online feature selection, improving stability and efficiency.

Findings

FIRES achieves competitive performance with simple models.

It significantly enhances feature selection stability.

The model complexity has minor impact on discriminative power.

Abstract

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.