Analyzing classification methods in multi-label tasks

Authors:
Araken M. Santos;Laura E. A. Santana;Anne M. Canuto
Affiliations:
Federal Rural University of Semi-Árido Angicos, RN, Brazil;Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

Multi-label classification methods have been increasingly used in modern application, such as music categorization, functional genomics and semantic annotation of images. This paper presents a comparative analysis of some existing multi-label classification methods applied to different domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.