The impact of different parameter sets on the classification of asteroid types
- 1Institute of Computing Science, Poznań University of Technology, Poznań, Poland
- 2Astronomical Observatory Insitute, A. Mickiewicz University, Poznań, Poland
- 3Department of Earth Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Massachusetts, USA
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.
How to cite: Klimczak, H., Kotłowski, W., Oszkiewicz, D., DeMeo, F., Kryszczyńska, A., Kwiatkowski, T., and Wilawer, E.: The impact of different parameter sets on the classification of asteroid types, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-807, https://doi.org/10.5194/epsc2021-807, 2021.
