使用机器学习算法和深度学习的恒星-星系分类

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
A. Savyanavar, Nikhil C. Mhala, Shiv H. Sutar
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引用次数: 2

摘要

宇宙学是研究由恒星和星系组成的宇宙的学科。望远镜的进步使得捕捉高分辨率图像成为可能,这些图像可以使用机器学习(ML)算法进行分析。本文利用ML算法将恒星星系数据集分为恒星和星系两类,并比较了它们的分类性能。观察到,与其他ML分类器相比,随机森林提供了78%的更好准确率。为了进一步提高分类精度,我们提出了CNN(卷积神经网络)模型,准确率达到92.44%。由于CNN模型本身提取了特征,因此具有较高的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Star-Galaxy classification using machine learning algorithms and deep learning
Cosmology is the study of the universe comprising stars and galaxies. Advancement in the telescope has made it possible to capture high-resolution images, which can be analyzed using machine learning (ML) algorithms. This paper classifies the star galaxy dataset into two classes: star and galaxy using ML algorithms and compares their classification performance. It is observed that random forest provides better accuracy of 78% as compared to other ML classifiers. Further to improve the classification accuracy, we proposed a CNN (Convolution Neural Network) model and achieved an accuracy of 92.44%. Since the CNN model itself extracts the characteristics, it exhibits superior classification accuracy.
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