Explainable classification of astronomical uncertain time series

Autor: Mbouopda, Michael, Ishida, Emille, Mephu Nguifo, Engelbert, Gangler, Emmanuel
Přispěvatelé: Mbouopda, Michael, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Université Clermont Auvergne (UCA), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
FOS: Computer and information sciences
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Computer Science - Machine Learning
Time series
Computer Science - Artificial Intelligence
Astronomy
Uncertainty2622889
[PHYS.ASTR.GA] Physics [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA]
Uncertainty
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
FOS: Physical sciences
[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
Classification
Explainability
Astrophysics - Astrophysics of Galaxies
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Machine Learning (cs.LG)
[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]
[PHYS.ASTR.GA]Physics [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA]
Artificial Intelligence (cs.AI)
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
[PHYS.ASTR.CO] Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]
Astrophysics of Galaxies (astro-ph.GA)
Popis: Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transient sources, which are modeled as uncertain time series. Although black-box methods achieve appreciable performance, existing interpretable time series methods failed to obtain acceptable performance for this type of data. Furthermore, data uncertainty is rarely taken into account in these methods. In this work, we propose an uncertaintyaware subsequence based model which achieves a classification comparable to that of state-of-the-art methods. Unlike conformal learning which estimates model uncertainty on predictions, our method takes data uncertainty as additional input. Moreover, our approach is explainable-by-design, giving domain experts the ability to inspect the model and explain its predictions. The explainability of the proposed method has also the potential to inspire new developments in theoretical astrophysics modeling by suggesting important subsequences which depict details of light curve shapes. The dataset, the source code of our experiment, and the results are made available on a public repository.
Databáze: OpenAIRE