Improvement of pulsars detection using dataset balancing methods and symbolic classification ensemble

Abstract

Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5-fold cross-validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1-score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the $ACC=0.978$, $AUC=0.9452$ , $Precision=0.905$, $Recall=0.9963$, and $F1-Score=0.94877$, on the original dataset.