Multi-label classification and extracting predicted class hierarchies

✍ Scribed by Florian Brucker; Fernando Benites; Elena Sapozhnikova

• Publisher: Elsevier Science
• Year: 2011
• Tongue: English
• Weight: 485 KB
• Volume: 44
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2010.09.010

✦ Synopsis

This paper investigates hierarchy extraction from results of multi-label classification (MC). MC deals with instances labeled by multiple classes rather than just one, and the classes are often hierarchically organized. Usually multi-label classifiers rely on a predefined class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system classifies multi-label data and extracts a class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner.