VERIFICATION OF MACHINE LEARNING METHODS FOR BINARY MORPHOLOGICAL CLASSIFICATION OF GALAXIES FROM SDSS - Vasylenko, M. Yu.; Dobrycheva, D. V.; Vavilova, I. B.; Melnyk, O. V.; Elyiv, A. A. (Одеські астрономічні публікації, 2019)
We present a study on the verifica- tion of Machine Learning methods to be applied for binary morphological classification of galaxies. With this aim we used the sample of 60561 galaxies from the SDSSDR9 survey with a redshift of 0 . 02 < z < 0 . 06 and absolute magnitudes of − 24 m < M r < − 19 . 4 m . We applied the following classification methods using own code in Python to predict correctly the morphology of Late and Early galaxies: Naive Bayes, Random Forest, Support Vector Machines, Logistic Regression, and k- Nearest Neighbor algorithm. To study the classifier, we used absolute magnitudes M u ,M g ,M r ,M i ,M z , color indices M u − M r ,M g − M i ,M u − M g ,M r − M z , and inverse concentration index to the center R50/R90. We compared these new results with previous one made with the KNIME Analytics Platform 3.5.3. It turned out that Random Forest and Support Vector Machine Classifiers provide a highest accuracy, as in the previous study, but with help our code in Python we increased an accuracy from 92.9 % of correctly classified (96% – E and 84% – L ) to 94,6% (96,9% – E and 89,7 % – L ). The accuracy of the remaining methods also grew by 88% to 93%. So, using these classifiers and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 60561 galaxies from the SDSSDR9 with unknown morphological types and found 22301 E and 38260 L types among them.