Abstract—Feature selection is an effective technique to reduce
the dimensionality of a data set and to select relevant features
for the domain problem. Recently, stability of feature selection
methods has gained increasing attention. In fact, it has become a
crucial factor in determining the goodness of a feature selection
algorithm besides the learning performance. In this work, we
conduct an extensive experimental study using verity of data sets
and different well-known feature selection algorithms in order to
study the behavior of these algorithms in terms of the stability.
Index Terms—Feature selection algorithms, stability, dimensionality
reduction, data distribution, Jaccard Index, sample size.