Drifting Features: Detection and evaluation in the context of automatic RRLs identification in VVV

no code implementations • 4 May 2021 • J. B. Cabral, M. Lares, S. Gurovich, D. Minniti, P. M. Granitto

However, small changes on the data over long distances or long periods of time, which cannot be easily detected by statistical methods, can be harmful to these methods.

Astronomy

From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning

no code implementations • 6 Sep 2018 • J. B. Cabral, B. Sánchez, F. Ramos, S. Gurovich, P. Granitto, J. Vanderplas

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases.

Astronomy BIG-bench Machine Learning +3