# Assessment of the influence of features on a classification problem: an application to COVID-19 patients

arXiv: https://arxiv.org/abs/2104.14958 · HTML: https://mlsanity.com/paper/2104.14958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Falling Rule Lists](https://mlsanity.com/paper/1411.5899/) (2014)
- [Visualizing the Feature Importance for Black Box Models](https://mlsanity.com/paper/1804.06620/) (2018)
- [Influence in Classification via Cooperative Game Theory](https://mlsanity.com/paper/1505.00036/) (2015)
- [A Feature Selection Based on Perturbation Theory](https://mlsanity.com/paper/1902.09938/) (2019)

## Built on by (most cited first, 1 of 1)

- [On the influence of dependent features in classification problems: a game-theoretic perspective](https://mlsanity.com/paper/2408.02481/) (2024)

## Abstract (arXiv)

This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.