# A scalable saliency-based Feature selection method with instance level information

## TL;DR

This paper introduces a scalable saliency-based feature selection method that leverages deep learning saliency techniques to identify relevant features at the instance level, improving interpretability for specific samples.

## Contribution

The paper presents a novel feature selection approach, Saliency-based Feature Selection (SFS), applicable across neural network architectures and trained with gradient descent, focusing on instance-specific relevance.

## Key findings

- Effective in neural networks and other gradient descent-trained models
- Provides instance-level feature relevance information
- Enhances interpretability of models for specific samples

## Abstract

Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction. This allows the creation of compact models that are easier to interpret. Most of these techniques work over the whole dataset, but they are unable to provide the user with successful information when only instance information is needed. In short, given any example, classic feature selection algorithms do not give any information about which the most relevant information is, regarding this sample. This work aims to overcome this handicap by developing a novel feature selection method, called Saliency-based Feature Selection (SFS), based in deep-learning saliency techniques. Our experimental results will prove that this algorithm can be successfully used not only in Neural Networks, but also under any given architecture trained by using Gradient Descent techniques.