The Imbalanced Problem in Morphological Galaxy Classification

Computing Research Repository, 2010

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the

Journal of Computational Interdisciplinary Sciences, 2016

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 Kmeans and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA ≥ 97%.

arXiv: Astrophysics of Galaxies, 2019

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0\% and 88.1\%, respectively. Particularly, the morphological parameter {\rm FWHM/PSF} performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclass...

Monthly notices of the Royal Astronomical Society, 2009

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 Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects.

Monthly Notices of the Royal Astronomical Society, 1996

We discuss the application of a class of machine learning algorithms known as decision trees to the process of galactic classification. In particular, we explore the application of oblique decision trees induced with different impurity measures to the problem of classifying galactic morphology data provided by Storrie-Lombardi et al. (1992). Our results are compared to those obtained by a neural network classifier created by Storrie-Lombardi et al, and we show that the two methodologies are comparable. We conclude with a demonstration that the original data can be easily classified into less well-defined categories.

Monthly Notices of the Royal Astronomical Society, 2017

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Astronomy and Computing

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.

IOP, 2021

With the advent of new high-resolution instruments for detecting and studying radio galaxies with different morphologies, the need for the use of automatic classification methods is undeniable. Here, we focused on the morphological-based classification of radio galaxies known as Fanaroff–Riley (FR) type I and type II via supervised machine-learning approaches. Galaxy images with a resolution of 5″ at 1.4 GHz provided by the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey are employed. The radial Zernike polynomials are exploited to extract image moments. Then, the rotation, translation, and scale-invariant moments of images are used to form a training set (65% of the radio galaxy sample) and a test set (the remaining 35%). The classes of the test set are determined by two classifiers: a support vector machine and a twin support vector machine (TWSVM). In addition the genetic algorithm is employed to optimize the length of moment series and to find the optimum valu...

Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by th...

Scientific Journal of Informatics

Purpose: The goal of this research is to create a precise prediction model that can differentiate between spiral and non-spiral galaxies using the Zoo galaxy dataset. Decision tree analysis and random forest models will be used to construct the model, and various conditions within the dataset will be employed to classify the data accurately. The model's performance will be evaluated using a confusion matrix, and the probability of predicting spiral galaxies will be analyzed. The research will also investigate the differences in Total Power among signal types and identify Peak Frequency and Bandwidth values consistent across all signal types. This study is expected to provide important insights into galaxy classification and signal characteristics, specifically in the fields of astronomy and astrophysics.Methods: This study utilized the decision tree analysis research method to create a predictive model for identifying spiral galaxies using the Zoo galaxy dataset. The research ap...