Feature selection with scalable variational gaussian process via sensitivity analysis based on L2 divergence

Younghwan Jeon, Ganguk Hwang

Abstract: Highlights•A new model agnostic feature selection method for supervised learning.•Sensitivity analysis with the predictive distribution of a scalable Gaussian process.•New L2 divergence derivation to better measure classification results and changes.•Better performance on several learning models than existing methods.