Assessing Features Importance in the 15-Class Galaxy Classification Problem

This study explores feature selection for classifying galaxy morphology using the extensive Galaxy Zoo 2 dataset. We investigate supervised and unsupervised learning methods to group galaxies based on key features, aiming to replicate supervised learning results. We evaluate various feature selection methods and compare them to an existing classification approach. Our results demonstrate that a reduced set of features based on adjusted vote fractions improves classification accuracy and potentially reduces computational complexity. While unsuper-vised clustering partially groups galaxies by morphology, further optimization is required. This work suggests that feature selection and unsupervised learning are promising techniques for the efficient classification of large galaxy datasets in upcoming astronomical surveys.