  Morphological classification is a key piece of information to define samples
of galaxies aiming to study the large-scale structure of the universe. In
essence, the challenge is to build up a robust methodology to perform a
reliable morphological estimate from galaxy images. Here, we investigate how to
substantially improve the galaxy classification within large datasets by
mimicking human classification. We combine accurate visual classifications from
the Galaxy Zoo project with machine and deep learning methodologies. We propose
two distinct approaches for galaxy morphology: one based on non-parametric
morphology and traditional machine learning algorithms; and another based on
Deep Learning. To measure the input features for the traditional machine
learning methodology, we have developed a system called CyMorph, with a novel
non-parametric approach to study galaxy morphology. The main datasets employed
comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also
discuss the class imbalance problem considering three classes. Performance of
each model is mainly measured by Overall Accuracy (OA). A spectroscopic
validation with astrophysical parameters is also provided for Decision Tree
models to assess the quality of our morphological classification. In all of our
samples, both Deep and Traditional Machine Learning approaches have over 94.5%
OA to classify galaxies in two classes (elliptical and spiral). We compare our
classification with state-of-the-art morphological classification from
literature. Considering only two classes separation, we achieve 99% of overall
accuracy in average when using our deep learning models, and 82% when using
three classes. We provide a catalog with 670,560 galaxies containing our best
results, including morphological metrics and classification.
