A systematic comparison of ten machine learning classifiers on Dark Energy Survey galaxy images shows that convolutional neural networks outperform classical methods, reaching roughly 99 percent accuracy after the authors relabel galaxies they believe Galaxy Zoo 1 misclassified.
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Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
A systematic comparison of ten machine learning classifiers on Dark Energy Survey galaxy images shows that convolutional neural networks outperform classical methods, reaching roughly 99 percent accuracy after the authors relabel galaxies they believe Galaxy Zoo 1 misclassified.