Using slisemap to interpret physical data.
PLoS One
; 19(1): e0297714, 2024.
Article
em En
| MEDLINE
| ID: mdl-38271355
ABSTRACT
Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
PLoS One
Ano de publicação:
2024
Tipo de documento:
Article