  We describe the problem of aggregating the label predictions of diverse
classifiers using a class taxonomy. Such a taxonomy may not have been available
or referenced when the individual classifiers were designed and trained, yet
mapping the output labels into the taxonomy is desirable to integrate the
effort spent in training the constituent classifiers. A hierarchical taxonomy
representing some domain knowledge may be different from, but partially
mappable to, the label sets of the individual classifiers. We present a
heuristic approach and a principled graphical model to aggregate the label
predictions by grounding them into the available taxonomy. Our model aggregates
the labels using the taxonomy structure as constraints to find the most likely
hierarchically consistent class. We experimentally validate our proposed method
on image and text classification tasks.
