The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extra-galactic objects and identification of supernovae.
@article{arxiv.1601.03931,
title = {An analysis of feature relevance in the classification of astronomical transients with machine learning methods},
author = {Antonio D'Isanto and Stefano Cavuoti and Massimo Brescia and Ciro Donalek and Giuseppe Longo and Giuseppe Riccio and Stanislav G. Djorgovski},
journal= {arXiv preprint arXiv:1601.03931},
year = {2016}
}