Optimizing Hierarchical Classification with Adaptive Node Collapses

Data intensive solutions, such as solutions that include machine learning components, are becoming more and more prevalent. The standard way of developing such solutions is to train machine learning models with manually annotated or labeled data for a given task. This methodology assumes the existence of ample human annotated data. Unfortunately, this is often not the case, due to imbalanced distribution of classes and lack of human annotation resources. This challenge is exasperated when thousands of hierarchical classes are introduced. Therefore, it is critical to quantify the sufficiency of the data for a given task before applying standard machine learning algorithms. Moreover, it may be the case that there is ample labeled training data to only solve a sub-problem. In particular, in the hierarchical classification problem, the sufficiency level of training data could vary significantly depending on the granularity level of hierarchy we use for classification. We identify a need to decompose the given problem to sub-problems for which there is ample training data. In this paper we propose a methodology to decompose a hierarchical classification problem considering the characteristics of a given dataset. We define an optimization problem of adaptive node collapse that identifies an appropriate hierarchy decomposition based on a trade-off between multiple goals. In our experiments, we consider the trade-off between the learning accuracy and the hierarchy abstraction level.