Improving galaxy morphology with machine learning
  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%.
