Imbalanced Ensemble Learning for Enhanced Pulsar Identification

Abstract

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.

Novel Oversampling Algorithm for Handling Imbalanced Data Classification

“…Table I nominates a list of selected applications with data domain analysis and categorization as binary, multiclass, or of both binary and multiclass imbalance. Binary classification techniques are the most progressive technique to deal with several applications such as medical diagnosis, and fault-finding activities in various business domains which always put forth the statistical results either belonging to one category of data or belonging to a second category [18,19,20]. To deal with the classification analysis of these binary and multiclass imbalance data applications, numerous approaches are discussed in the upcoming sections.…”