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Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction

Fürnkranz, Johannes and Park, Sang-Hyeun
Ganascia, Jean-Gabriel and Lenca, Philippe and Petit, Jean-Marc (eds.) (2012):
Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction.
Lyon, France, Springer, In: Proceedings of the 15th International Conference on Discovery Science (DS-12), Lyon, France, [Conference or Workshop Item]

Abstract

In this paper, we reinterpret error-correcting output codes (ECOCs) as a framework for converting multi-class classification problems into multi-label prediction problems. Different well-known multi-label learning approaches can be mapped upon particular ways of dealing with the original multi-class problem. For example, the label powerset approach obviously constitutes the inverse transformation from multi-label back to multi-class, whereas binary relevance learning may be viewed as the conventional way of dealing with ECOCs, in which each classifier is learned independently of the others. Consequently, we evaluate whether alternative choices for solving the multi-label problem may result in improved performance. This question is interesting because it is not clear whether approaches that do not treat the bits of the code words independently have sufficient error-correcting properties. Our results indicate that a slight but consistent advantage can be obtained with the use of multi-label methods, in particular when longer codes are employed.

Item Type: Conference or Workshop Item
Erschienen: 2012
Editors: Ganascia, Jean-Gabriel and Lenca, Philippe and Petit, Jean-Marc
Creators: Fürnkranz, Johannes and Park, Sang-Hyeun
Title: Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction
Language: English
Abstract:

In this paper, we reinterpret error-correcting output codes (ECOCs) as a framework for converting multi-class classification problems into multi-label prediction problems. Different well-known multi-label learning approaches can be mapped upon particular ways of dealing with the original multi-class problem. For example, the label powerset approach obviously constitutes the inverse transformation from multi-label back to multi-class, whereas binary relevance learning may be viewed as the conventional way of dealing with ECOCs, in which each classifier is learned independently of the others. Consequently, we evaluate whether alternative choices for solving the multi-label problem may result in improved performance. This question is interesting because it is not clear whether approaches that do not treat the bits of the code words independently have sufficient error-correcting properties. Our results indicate that a slight but consistent advantage can be obtained with the use of multi-label methods, in particular when longer codes are employed.

Place of Publication: Lyon, France
Publisher: Springer
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
Event Title: Proceedings of the 15th International Conference on Discovery Science (DS-12)
Event Location: Lyon, France
Date Deposited: 26 Nov 2015 08:05
Identification Number: doi:10.1007/978-3-642-33492-4_21
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