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DenseNet-random forest model based galaxy classification
- 1 Beijing Jiaotong University
* Author to whom correspondence should be addressed.
Abstract
Finding an efficient and accurate adaptive method that can automatically classify galaxies has become an industry consensus. However, most of the current studies on galaxy classification use a single model for direct output, without considering the combination with other models to output more satisfactory prediction results. Through convolutional neural network and classifier, this study studied the possibility of applying the deep learning model to the Galaxy 10 DECals dataset classification, and proposed DenseNet-Random Forest model through comparative analysis. By adjusting and training DenseNet-121 with appropriate hyperparameters, the input tensor is transferred to the basic model through the creation of a shape input layer, where GlobalAveragePooling2D is added to perform an average pooling operation on each feature map, reducing the spatial dimension of each feature map to 1. During the process, a complete connection layer with 64 neurons was added using the ReLU activation function, and a Dropout layer was added to randomly discard 20% of the neurons during training to prevent overfitting. In addition, ReLU Activation function with 32 full connection layers of neurons and softmax Activation function with 10 output layers of neurons are added. By acquiring the feature vector of the training model and the real label of the verification set, assign x and y values respectively, and import them into the Random forest classifier model. The experimental results demonstrated the model ultimately achieved a prediction accuracy of 68% when processing the Galaxy 10 DECals dataset, and achieved nearly 30% improvement in Precision, Recall, and F1 scores.
Keywords
DenseNet, random forest, galaxy classification
[1]. NASA 2023 Types NASA https://universe.nasa.gov/galaxies/types/.
[2]. Fang G et al 2023 Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set. The Astronomical Journal 165(2) 35.
[3]. Tarsitano F et al 2022 Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning. Monthly Notices of the Royal Astronomical Society 511(3) pp 3330-3338.
[4]. Ghadekar P et al 2023 Galaxy Classification Using Deep Learning In Advancements in Smart Computing and Information Security: First International Conference ASCIS 2022 Rajkot India November 24–26 Revised Selected Papers, Part I pp 3-13 Cham: Springer Nature Switzerland.
[5]. Radhamani P S et al 2022 An Effective Galaxy Classification Using Fractal Analysis and Neural Network In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) pp 19-24 IEEE.
[6]. Ćiprijanović A et al 2022 DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification Machine Learning: Science and Technology 3(3) 035007.
[7]. Bernal S S et al 2023 Galaxy Classification Upgraded: Boosting Machine Learning with the Fourier Transform Available at SSRN 4428890.
[8]. Henrysky 2022 (n.d.) Henrysky/Galaxy10: A CIFAR10-like galaxy image dataset GitHub https://github.com/henrysky/Galaxy10.
[9]. Huang G 2018 Densely connected Convolutional Networks arXiv.org https://doi.org/10.48550/arXiv.1608.06993.
[10]. Yu Q Yang Y Lin Z et al 2020 Improved denoising autoencoder for maritime image denoising and semantic segmentation of USV China Communications 17(3): 46-57.
[11]. Kazakov O D Mikheenko O V 2020 Transfer learning and domain adaptation based on modeling of socio-economic systems Бизнес-информатика 14 7-20.
Cite this article
Hu,J. (2023). DenseNet-random forest model based galaxy classification. Applied and Computational Engineering,22,100-105.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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