  Hierarchical classification is supervised multi-class classification problem
over the set of class labels organized according to a hierarchy. In this
report, we study the work by Ramaswamy et. al. on hierarchical classification
over symmetric tree distance loss. We extend the consistency of hierarchical
classification algorithm over asymmetric tree distance loss. We design a
$\mathcal{O}(nk\log{}n)$ algorithm to find Bayes optimal classification for a
k-ary tree as a hierarchy. We show that under reasonable assumptions over
asymmetric loss function, the Bayes optimal classification over this asymmetric
loss can be found in $\mathcal{O}(k\log{}n)$. We exploit this insight and
attempt to extend the Ova-Cascade algorithm \citet{ramaswamy2015convex} for
hierarchical classification over the asymmetric loss.
