  This project outlines the complete development of a variable star
classification algorithm methodology. With the advent of Big-Data in astronomy,
professional astronomers are left with the problem of how to manage large
amounts of data, and how this deluge of information can be studied in order to
improve our understanding of the universe. While our focus will be on the
development of machine learning methodologies for the identification of
variable star type based on light curve data and associated information, one of
the goals of this work is the acknowledgment that the development of a true
machine learning methodology must include not only study of what goes into the
service (features, optimization methods) but a study on how we understand what
comes out of the service (performance analysis). The complete development of a
beginning-to-end system development strategy is presented as the following
individual developments (simulation, training, feature extraction, detection,
classification, and performance analysis). We propose that a complete machine
learning strategy for use in the upcoming era of big data from the next
generation of big telescopes, such as LSST, must consider this type of design
integration.
