Hierarchical Classification Using Binary Data

  • Denali Molitor University of California, Los Angeles
  • Deanna Needell University of California, Los Angeles


In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we show case computational and accuracy advantages.

How to Cite
Molitor, D., & Needell, D. (2019). Hierarchical Classification Using Binary Data. AI Magazine, 40(2), 59-65. https://doi.org/10.1609/aimag.v40i2.2846