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Semantic Description of Explainable Machine Learning Workflows for Improving Trust.
- Source :
- Applied Sciences (2076-3417); Nov2021, Vol. 11 Issue 22, p10804, 18p
- Publication Year :
- 2021
-
Abstract
- Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
COVID-19 pandemic
SUPPORT vector machines
WORKFLOW
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 11
- Issue :
- 22
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 153790364
- Full Text :
- https://doi.org/10.3390/app112210804