Streaming Classification of Variable Stars. (arXiv:1912.02235v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Zorich_L/0/1/0/all/0/1">Lukas Zorich</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pichara_K/0/1/0/all/0/1">Karim Pichara</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Protopapas_P/0/1/0/all/0/1">Pavlos Protopapas</a>
In the last years, automatic classification of variable stars has received
substantial attention. Using machine learning techniques for this task has
proven to be quite useful. Typically, machine learning classifiers used for
this task require to have a fixed training set, and the training process is
performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope
(LSST) will generate new observations daily, where an automatic classification
system able to create alerts online will be mandatory. A system with those
characteristics must be able to update itself incrementally. Unfortunately,
after training, most machine learning classifiers do not support the inclusion
of new observations in light curves, they need to re-train from scratch.
Naively re-training from scratch is not an option in streaming settings, mainly
because of the expensive pre-processing routines required to obtain a vector
representation of light curves (features) each time we include new
observations. In this work, we propose a streaming probabilistic classification
model; it uses a set of newly designed features that work incrementally. With
this model, we can have a machine learning classifier that updates itself in
real time with new observations. To test our approach, we simulate a streaming
scenario with light curves from CoRot, OGLE and MACHO catalogs. Results show
that our model achieves high classification performance, staying an order of
magnitude faster than traditional classification approaches.
In the last years, automatic classification of variable stars has received
substantial attention. Using machine learning techniques for this task has
proven to be quite useful. Typically, machine learning classifiers used for
this task require to have a fixed training set, and the training process is
performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope
(LSST) will generate new observations daily, where an automatic classification
system able to create alerts online will be mandatory. A system with those
characteristics must be able to update itself incrementally. Unfortunately,
after training, most machine learning classifiers do not support the inclusion
of new observations in light curves, they need to re-train from scratch.
Naively re-training from scratch is not an option in streaming settings, mainly
because of the expensive pre-processing routines required to obtain a vector
representation of light curves (features) each time we include new
observations. In this work, we propose a streaming probabilistic classification
model; it uses a set of newly designed features that work incrementally. With
this model, we can have a machine learning classifier that updates itself in
real time with new observations. To test our approach, we simulate a streaming
scenario with light curves from CoRot, OGLE and MACHO catalogs. Results show
that our model achieves high classification performance, staying an order of
magnitude faster than traditional classification approaches.
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