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Pulsars can be detected based on their emitted radio waves. Machine learning methods can be employed to support automated screening of a large number of radio signals for pulsars. This is however a challenging task since training these methods is affected by an inherent imbalance in the acquired data with signals relating to actual pulsars being in the minority. In this paper, we demonstrate that ensemble classification methods that are dedicated to imbalanced classification problems can be successfully employed for pulsar identification. Classifier ensembles combine several individual classifiers to yield more robust and improved classification, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Experimental results, based on HTRU2 data, show that the investigated ensembles outperform methods that do not consider class balance, and suggest their use for other applications in astrophysics.

Imbalanced Ensemble Learning for Enhanced Pulsar Identification