Open AccessHierarchical Classification Using Binary Data
Author(s) -
Molitor Denali,
Needell Deanna
Publication year - 2019
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v40i2.2846
Subject(s) - categorization , computer science , class (philosophy) , binary number , simple (philosophy) , binary classification , data mining , artificial intelligence , binary data , hierarchical database model , machine learning , pattern recognition (psychology) , support vector machine , mathematics , philosophy , arithmetic , epistemology
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 showcase computational and accuracy advantages.