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Activity Number: 131 - Predictive Modeling in Data Science
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323099
Title: An Overview of Existing and a Novel Approaches to Multi-Label Classification
Author(s): Hyukjun Gweon* and Matthias Schonlau and Stefan Steiner
Companies: and University of Waterloo and University of Waterloo
Keywords: Multi-label classification ; Label correlation ; Machine learning
Abstract:

Multi-label classification is a supervised learning problem where an observation may be associated with multiple binary (outcome) labels simultaneously. We give an overview over common approaches to multi-label classification and also introduce a new approach as an extension of the nearest neighbor principle. Experiments on benchmark multi-label data sets show that the proposed method on average outperforms other commonly used approaches in terms of classification performance.


Authors who are presenting talks have a * after their name.

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