Pulsar Candidate Selection Using Gaussian Hellinger Extremely Fast Decision Tree

Abstract

Radio wave data gathered by pulsar finding telescopes are required to be classified while being streamed. The reason for that is the practical constraints of traditional machine learning algorithms on streaming datasets. Traditional machine learning algorithms would take considerable compute power, memory and time to give pragmatic results.(recent surveys collect data at the rate of 0.5 – 1 terabyte per second) Stream classification algorithms are specifically developed to address the above limitations and can classify data streams without taking up a lot of memory or training time. They relate with characteristics of data streams such as concept drift and limited memory. Extremely Fast Decision Tree is one of the stream classification algorithms that can learn incrementally when it sees new data. However, data from pulsar detecting datastreams are highly imbalanced (there are less examples of pulsars in the data than non-pulsar objects). Learning incrementally from such a datastream would be a destructive interference for the model’s precision (of detecting pulsars). In this research, we introduce an improved version of the Extremely Fast Decision Tree, that is able to learn imbalanced data streams. Our approach is fast, accurate, and avoids the pitfalls of class imbalance and concept drift.