  Throughout the processing and analysis of survey data, a ubiquitous issue
nowadays is that we are spoilt for choice when we need to select a methodology
for some of its steps. The alternative methods usually fail and excel in
different data regions, and have various advantages and drawbacks, so a
combination that unites the strengths of all while suppressing the weaknesses
is desirable. We propose to use a two-level hierarchy of learners. Its first
level consists of training and applying the possible base methods on the first
part of a known set. At the second level, we feed the output probability
distributions from all base methods to a second learner trained on the
remaining known objects. Using classification of variable stars and photometric
redshift estimation as examples, we show that the hierarchical combination is
capable of achieving general improvement over averaging-type combination
methods, correcting systematics present in all base methods, is easy to train
and apply, and thus, it is a promising tool in the astronomical "Big Data" era.
