Fast Estimation Method for the Stability of Ensemble Feature Selectors
This work addresses a computational bottleneck for researchers and practitioners using ensemble feature selection in machine learning, though it appears incremental as it builds on existing ensembling techniques.
The paper tackles the problem of high computational cost in estimating the stability of ensemble feature selectors, proposing a simulator method that reduces computation time both theoretically and empirically.
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.