PERFORMANCE COMPARISON OF SUPERVISED MACHINE LEARNING METHODS IN CLASSIFYING CELESTIAL OBJECTS

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dc.contributor.authorEr, Maide Feyza
dc.contributor.authorBilgin, Turgay Tugay
dc.date.accessioned2026-02-08T15:08:23Z
dc.date.available2026-02-08T15:08:23Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn recent times, astronomy has entered a new era with rapidly growing data sources and advanced observation techniques. The construction of powerful telescopes has enabled the collection of spectral data from millions of celestial objects. However, the increasing number and variety of data have made it challenging to categorize these celestial objects. This study employs machine learning methods to address the fundamental problem of classifying stars, galaxies, and quasars in astronomy. The dataset underwent detailed preprocessing to identify effective features for classification. KNIME Analytics Platform was used for data analysis and visualization, facilitating rapid and efficient data analysis through its drag-and-drop interface. Among the machine learning methods used in our study—Decision Trees, Random Forest, and Naive Bayes—the highest accuracy rate of 97.86% was achieved with the Random Forest model. Notably, despite its lower overall performance compared to other models, the Naive Bayes classifier exhibited superior performance in distinguishing the STAR class, which is one of the study's interesting findings. Future studies aim to enhance model accuracy by using larger and more diverse datasets and exploring different machine learning algorithms. Additionally, the impact of deep learning methods on classification performance will be investigated.
dc.identifier.doi10.34248/bsengineering.1517904
dc.identifier.endpage970
dc.identifier.issn2619-8991
dc.identifier.issue5
dc.identifier.startpage960
dc.identifier.trdizinid1265563
dc.identifier.urihttps://doi.org/10.34248/bsengineering.1517904
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4986
dc.identifier.volume7
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBlack Sea Journal of Engineering and Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260207
dc.subjectNaive Bayes
dc.subjectMachine learning
dc.subjectClassification
dc.subjectRandom forest
dc.subjectDecision tree
dc.titlePERFORMANCE COMPARISON OF SUPERVISED MACHINE LEARNING METHODS IN CLASSIFYING CELESTIAL OBJECTS
dc.typeArticle

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