  The amount of collected data in many scientific fields is increasing, all of
them requiring a common task: extract knowledge from massive, multi parametric
data sets, as rapidly and efficiently possible. This is especially true in
astronomy where synoptic sky surveys are enabling new research frontiers in the
time domain astronomy and posing several new object classification challenges
in multi dimensional spaces; given the high number of parameters available for
each object, feature selection is quickly becoming a crucial task in analyzing
astronomical data sets. Using data sets extracted from the ongoing Catalina
Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a
variety of feature selection strategies used to identify the subsets that give
the most information and the results achieved applying these techniques to
three major astronomical problems.
