A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection

Li, Shijia; Feng, Sibo; Guo, Ping; Yin, Qian

doi:10.1109/cec.2018.8477886

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…e study [17] focused on pulsar classification using hierarchical deep neural network (DNN). To reduce the training time of DNN, pseudoinverse learning (PIL) is preferred over the gradient descent (GD) method.…”

Section: Related Workmentioning

confidence: 99%

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Lee

Rustam

Aljedaani

et al. 2021

Advances in Astronomy

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Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.

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Section: Related Workmentioning

confidence: 99%

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Lee

Rustam

Aljedaani

et al. 2021

Advances in Astronomy

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“…This is called the early-fusion strategy, which was first proposed in Gunes & Piccardi (2005). In the early-fusion, the features are blended before classification; in the meanwhile, the late-fusion strategy proposed in Zhu et al (2014) and Li et al (2018) blends the classification results after the classifying operation. Finally, the blended feature is fed into a binary classifier to filter the positive candidates.…”

Section: Introductionmentioning

confidence: 99%

Early-fusion based pulsar identification with smart under-sampling

Shichuan

Kong

Zhou

et al. 2021

Res. Astron. Astrophys.

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The discovery of pulsars is of great significance in the field of physics and astronomy. As the astronomical equipment produces a large number of pulsar data, an algorithm for automatically identifying pulsars becomes urgent. We propose a deep learning framework for pulsar recognition. In response to the extreme imbalance between positive and negative examples and the hard negative sample issue presented in the High Time Resolution Universe Medlat Training Data, there are two coping strategies in our framework: the smart under-sampling and the improved loss function. We also apply the early-fusion strategy to integrate features obtained from different attributes before classification to improve the performance. To our best knowledge, this is the first study that integrates these strategies and techniques in pulsar recognition. The experiment results show that our framework outperforms previous works with respect to either the training time or F1 score. We can not only speed up the training time by 10 × compared with the state-of-the-art work, but also get a competitive result in terms of F1 score.

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mentioning

confidence: 99%

Early-fusion Based Pulsar Identification with Smart Under-sampling

Zhang,

Kong,

Zhou

et al. 2021

Preprint

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The discovery of pulsars is of great significance in the field of physics and astronomy. As the astronomical equipment produces a large amount of pulsar data, an algorithm for automatically identifying pulsars becomes urgent. We propose a deep learning framework for pulsar recognition. In response to the extreme imbalance between positive and negative examples and the hard negative sample issue presented in the HTRU Medlat Training Data, there are two coping strategies in our framework: the smart under-sampling and the improved loss function. We also apply the early-fusion strategy to integrate features obtained from different attributes before classification to improve the performance. To our best knowledge, this is the first study that integrates these strategies and techniques together in pulsar recognition. The experiment results show that our framework outperforms previous works with the respect to either the training time or F 1 score. We can not only speed up the training time by 10X compared with the state-of-the-art work, but also get a competitive result in terms of F 1 score.

show abstract

A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection

Cited by 6 publications

References 13 publications

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Early-fusion based pulsar identification with smart under-sampling

Early-fusion Based Pulsar Identification with Smart Under-sampling

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