Fast Estimation Method for the Stability of Ensemble Feature Selectors

TL;DR

This paper introduces a fast, simulation-based method to estimate the stability of ensemble feature selectors, significantly reducing computational costs while maintaining accuracy, thereby enhancing interpretability and robustness in feature selection.

Contribution

It presents the first approach to efficiently estimate ensemble feature selector stability using a novel simulator, reducing computational time both theoretically and empirically.

Findings

The proposed method accurately estimates stability with less computation.

It outperforms existing methods in speed while maintaining accuracy.

The approach is validated through theoretical analysis and empirical experiments.

Abstract

It is preferred that feature selectors be \textit{stable} for better interpretabity and robust prediction. Ensembling is known to be effective for improving the stability of feature selectors. Since ensembling is time-consuming, it is desirable to reduce the computational cost to estimate the stability of the ensemble feature selectors. We propose a simulator of a feature selector, and apply it to a fast estimation of the stability of ensemble feature selectors. To the best of our knowledge, this is the first study that estimates the stability of ensemble feature selectors and reduces the computation time theoretically and empirically.