  In big data applications such as healthcare data mining, due to privacy
concerns, it is necessary to collect predictions from multiple information
sources for the same instance, with raw features being discarded or withheld
when aggregating multiple predictions. Besides, crowd-sourced labels need to be
aggregated to estimate the ground truth of the data. Because of the imperfect
predictive models or human crowdsourcing workers, noisy and conflicting
information is ubiquitous and inevitable. Although state-of-the-art aggregation
methods have been proposed to handle label spaces with flat structures, as the
label space is becoming more and more complicated, aggregation under a label
hierarchical structure becomes necessary but has been largely ignored. These
label hierarchies can be quite informative as they are usually created by
domain experts to make sense of highly complex label correlations for many
real-world cases like protein functionality interactions or disease
relationships.
  We propose a novel multi-source hierarchical prediction consolidation method
to effectively exploits the complicated hierarchical label structures to
resolve the noisy and conflicting information that inherently originates from
multiple imperfect sources. We formulate the problem as an optimization problem
with a closed-form solution. The proposed method captures the smoothness
overall information sources as well as penalizing any consolidation result that
violates the constraints derived from the label hierarchy. The hierarchical
instance similarity, as well as the consolidation result, are inferred in a
totally unsupervised, iterative fashion. Experimental results on both synthetic
and real-world datasets show the effectiveness of the proposed method over
existing alternatives.
