Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Ernesto Lee,Wajdi Aljedaani,Furqan Rustam,Abid Ishaq,Vaibhav Rupapara,Imran Ashraf,Jianguo Yan

doi:10.1155/2021/4916494

Abstract

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.

Highlights

Pulsar star represents a stellar remnant often formed by the remains of a collapsed giant star
To improve the performance of machine learning models, data resampling is carried out using the cluster centroids (CC) technique. e CC technique is used for data balancing and reduces the chances of the model overfitting. e CC technique is an undersampling approach that reduces the number of samples of the majority class by randomly selecting the records and removing them, making the number of samples of the majority and minority class equal
Despite a decrease in the performance of different models, random forest (RF) shows the best performance with the undersampled data and achieves 0.943 accuracy score and 0.940 F1 score. e performance of other classifiers is similar except for multilayer perceptron (MLP) which achieves an accuracy of 0.905 and F1 score of 0.898

Summary

Introduction

Pulsar star represents a stellar remnant often formed by the remains of a collapsed giant star. Pulsar stars are very important for scientists to study nuclear physics, general relativity, gravitational waves, and factors leading to the collisions of black holes. Looking at a particular point through the telescope, they noticed radiation pulses and named them little green men 1 (LGM1). Later these unidentified objects were termed pulsars due to emission as pulses. They are called the pulsating source of radiation (PSR), and B1919 + 12 (PSR B1919 + 21) shows the position of the pulsar in the sky [2]. It is very difficult to detect a true pulsar star. Under certain conditions, detection is possible such as when angled at earth or X-rays burst caused by the detonataion known as supernova

Methods

Results

Conclusion