Many real world classification problems involve classes organized in a hierarchical tree-like structure. However in many cases the hierarchical structure is ignored and each class is treated in isolation or in other words the class structure is flattened (Dumais and Chen, 2000). In this paper, we propose a new approach of incorporating hierarchical structure knowledge by cascading it as an additional feature for Child level classifier. We posit that our cascading model will outperform the baseline “flat” model. Our empirical experiment provides strong evidences supporting our proposal. Interestingly, even imperfect hierarchical structure knowledge would also improve classification performance.
Ngo-Ye, Thomas, "Text Classification with Imperfect Hierarchical Structure Knowledge" (2010). AMCIS 2010 Proceedings. 112.