Assessment of the influence of features on a classification problem: an application to COVID-19 patients
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 9 Apr 2021.
Builds on
6 of 27 references.
References · 6
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
- 2014Falling Rule Listscited by 58
- 2018Visualizing the Feature Importance for Black Box Modelscited by 55
- 2015Influence in Classification via Cooperative Game Theorycited by 7
- 2019A Feature Selection Based on Perturbation Theorycited by 2
Built on by
Citing papers per year: 2024: 1.
1 paper.