Title: HiNet: Hierarchical Classification with Neural Network,
Abstract: Traditionally, classifying large hierarchical labels with more than 10000
distinct traces can only be achieved with flatten labels. Although flatten
labels is feasible, it misses the hierarchical information in the labels.
Hierarchical models like HSVM by \cite{vural2004hierarchical} becomes
impossible to train because of the sheer number of SVMs in the whole
architecture. We developed a hierarchical architecture based on neural networks
that is simple to train. Also, we derived an inference algorithm that can
efficiently infer the MAP (maximum a posteriori) trace guaranteed by our
theorems. Furthermore, the complexity of the model is only $O(n^2)$ compared to
$O(n^h)$ in a flatten model, where $h$ is the height of the hierarchy.