Classification of pulsar signals using ensemble gradient boosting algorithms based on asymmetric under-sampling method

Abstract The detection of pulsar signals is a highly intensive task. Numerous artificial intelligence (AI) and machine learning techniques (ML) have been proposed to classify pulsar and non-pulsar signals. While existing machine learning techniques improve classification efficiency, these methods are limited when it comes to dealing with large volumes of astronomical data, the extreme problem of class imbalance , and the polarization of high recall and precision. In this paper, to accurately classify pulsar and non-pulsar signals, extreme Gradient Boosting (XgBoost), and Light Gradient Boosting Machine (LightGBM) algorithms based on an asymmetric undersampling method are proposed. Firstly, the proposed model uses an asymmetric undersampling technique that divides the benchmark datasets into 75 subsets for the LOFAR Tied-Array All-Sky Survey (LOTAAS-1) and 9 subsets for the High Time Resolution Universe Pulsar Survey (HTRU2), eluding the class imbalance problem. Finally, XgBoost and LightGBM algorithms have been adopted for each new data subset, with a majority voting classifier being used to integrate the output of proposed models against each subset. Results of the proposed method were compared to state-of-the-art baseline models, which showed that our models significantly performed better than existing methods and gained almost 2% and 2.5% better F-score and precision results, respectively.