Abstract
The search of planets capable of sustaining life has been taken to a whole new level with NASA’s Kepler mission. The mission has successfully discovered around 4000 planets, however, the task of manual evaluation of this data is cumbersome and labor intensive, and calls for more efficient methods of discovering exoplanets to remove false positives and errors. The goal of this project is to utilize machine learning algorithms to classify stars as exoplanets through the data collected by the Kepler satellite. To this end, we plan to use preprocessing methods and apply suitable classification algorithms to build an accurate and optimal classifier, increasing the proficiency of the process.
All authors contributed equally.
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Sharma, H.K., Singh, B.K., Choudhury, T., Mohanty, S.N. (2023). PCA-Based Machine Learning Approach for Exoplanet Detection. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fourth International Conference on Computer and Communication Technologies. Lecture Notes in Networks and Systems, vol 606. Springer, Singapore. https://doi.org/10.1007/978-981-19-8563-8_44
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DOI: https://doi.org/10.1007/978-981-19-8563-8_44
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