This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA). Except concentration, all parameters performed a image segmentation pre-processing. For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo. With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects balanced dataset, we applied K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.
@article{arxiv.1705.06818,
title = {Improving galaxy morphology with machine learning},
author = {P. H. Barchi and F. G. da Costa and R. Sautter and T. C. Moura and D. H. Stalder and R. R. Rosa and R. R. de Carvalho},
journal= {arXiv preprint arXiv:1705.06818},
year = {2017}
}