| Autor: |
Barchi, P. H., da Costa, F. G., Sautter, R., Moura, T. C., Stalder, D. H., Rosa, R. R., de Carvalho, R. R. |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Journal of Computacional Interdisciplinary Sciences, v. 7, p. 114. 2016 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.6062/jcis.2016.07.03.0114 |
| Popis: |
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%. |
| Databáze: |
arXiv |
| Externí odkaz: |
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