Vollständiger Abstract
Worum geht es in dieser Arbeit?
The aim of the project is the classification of asteroids according to the most commonly used asteroid taxonomy (Bus-Demeo et al. 2009) with the use of various machine learning methods like Logistic Regression, Naive Bayes, Support Vector Machines, Gradient Boosting and Multilayer Perceptrons. Different parameter sets are used for classification in order to compare the quality of prediction with limited amount of data, namely the difference in performance between using the 0.45mu to 2.45mu spectral range and multiple spectral features, as well as performing the Prinicpal Component Analysis to reduce the dimensions of the spectral data. This work has been supported by grant No. 2017/25/B/ST9/00740 from the National Science Centre, Poland.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Hanna Klimczak, Wojciech Kotłowski, Dagmara Oszkiewicz, Francesca DeMeo, Agnieszka Kryszczyńska, Tomasz Kwiatkowski, Emil Wilawer
- Quelle
- Copernicus GmbH
- Publikation
- 2024-01-01
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Zitierfähiger Nachweis
Hanna Klimczak, Wojciech Kotłowski, Dagmara Oszkiewicz, Francesca DeMeo, Agnieszka Kryszczyńska, Tomasz Kwiatkowski, Emil Wilawer (2024). The impact of different parameter sets on the classification of asteroid types. https://doi.org/10.1044/2026_jslhr-25-00740