  The statistically equivalent signature (SES) algorithm is a method for
feature selection inspired by the principles of constrained-based learning of
Bayesian Networks. Most of the currently available feature-selection methods
return only a single subset of features, supposedly the one with the highest
predictive power. We argue that in several domains multiple subsets can achieve
close to maximal predictive accuracy, and that arbitrarily providing only one
has several drawbacks. The SES method attempts to identify multiple, predictive
feature subsets whose performances are statistically equivalent. Under that
respect SES subsumes and extends previous feature selection algorithms, like
the max-min parent children algorithm. SES is implemented in an homonym
function included in the R package MXM, standing for mens ex machina, meaning
'mind from the machine' in Latin. The MXM implementation of SES handles several
data-analysis tasks, namely classification, regression and survival analysis.
In this paper we present the SES algorithm, its implementation, and provide
examples of use of the SES function in R. Furthermore, we analyze three
publicly available data sets to illustrate the equivalence of the signatures
retrieved by SES and to contrast SES against the state-of-the-art feature
selection method LASSO. Our results provide initial evidence that the two
methods perform comparably well in terms of predictive accuracy and that
multiple, equally predictive signatures are actually present in real world
data.
