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