Feature relevance in morphological galaxy classification
نویسندگان
چکیده
منابع مشابه
Automatic morphological classification of galaxy images.
We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple W...
متن کاملA Hierarchical Model for Morphological Galaxy Classification
We propose a new method for the morphological galaxy classification which incorporates two main contributions: (i) the generation of artificial images of galaxies through geometric transformations to be used as additional examples in the training phase, (ii) the use of a novel hierarchical classifier for hierarchical galaxy classification. An additional classifier distinguishes galaxies from st...
متن کاملEnsembles of Classifiers for Morphological Galaxy Classification
We compare the use of three algorithms for performing automated morphological galaxy classiÐcation using a sample of 800 galaxies. ClassiÐers are created using a single training set as well as bootstrap replicates of the training set, producing an ensemble of classiÐers. We use a Naive Bayes classiÐer, a neural network trained with backpropagation, and a decision-tree induction algorithm with p...
متن کاملA New Non-Parametric Approach to Galaxy Morphological Classification
We present two new non-parametric methods for quantifying galaxy morphology: the relative distribution of the galaxy pixel flux values (the Gini coefficient or G) and the second-order moment of the brightest 20% of the galaxy’s flux (M20). We test the robustness of G and M20 to decreasing signal-to-noise and spatial resolution, and find that both measures are reliable to within 10% at average s...
متن کاملMachine Learning and Image Analysis for Morphological Galaxy Classification
In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We have used a neural network, and a locally weighted regression method, and also we implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble method, and manipulation of input features ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2000
ISSN: 0035-8711,1365-2966
DOI: 10.1046/j.1365-8711.2000.03525.x