Massive amount of available data in various forms such as text, image, and videos has mandated the need to provide a structured and organized view of the data to make it usable for data exploration and analysis. Hierarchical structure/taxonomies provides a natural and convenient way to organize information. Data organization using hierarchy has been extensively used in several domains - gene taxonomy for organizing gene sequences, DMOZ taxonomy for webpages, International patent classification hierarchy for browsing patent documents and ImageNet for indexing millions of images. Given, a hierarchy containing thousands of classes (or categories) and millions of instances (or examples), there is an essential need to develop an efficient and automated approaches to categorize unknown instances. This problem is referred to as Hierarchical Classification (HC) task. HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this tutorial, we will cover technical material related to large scale hierarchical classification. This will be meant for an audience with intermediate expertise in data mining having a background in classification (supervised learning). Formal definitions of hierarchical classification and variants will be discovered, along with a brief discussion on structured learning.

Link to tutorial: http://cs.gmu.edu/~mlbio/kdd2017tutorial.html