Abstract
The association between the instance query example and the class labels are mutually exclusive in traditional single label examples. But in real life applications like musical categorization, functional genomics, text, and document categorization, one instance query example may belong to a subset of class labels, i.e., mutually inclusive. Because of the highly correlated label structure, the traditional single label classification (SLC) algorithms will not be sufficient. We need effective algorithms to work with multiple labels. The multi-label classification (MLC) algorithms are classified into two ways: (1) transform the multi-label problem into single label binary problem and (2) make the existing single label algorithms to cope with multi-label problems. In this paper, we present theoretical concepts behind multi-label classification (MLC) and also we did a comparative analysis of transformation methods with two tools MEKA and MULAN over different application domains. 6 Example based, 6 Label based, and 4 Ranking-based measures are used to evaluate the efficacy of the different transformation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)
Elisseeff, A., Weston, J.: A Kernel method for multi-labelled classification. In: Proceedings of the Annual ACM Conference on Research and Development in Information Retrieval, pp. 274–281 (2005)
Read, J.: A pruned problem transformation method for multi-label classification. In: Proceedings of 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008), pp. 143–150 (2008)
Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: ICDM’08: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, vol. 0, pp. 995–1000, Washington, DC, USA. IEEE Computer Society (2008)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. In: Proceedings of the 9th International Conference on Music Information Retrieval, pp. 320–330 (2008)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Berlin (2010)
Tsoumakas, G., Vlahavas, I.: Random k-Labelsets: an ensemble method for multilabel classification. In: Proceedings of the 18th European Conference on Machine Learning (ECML 2007), pp. 406–417, Warsaw, Poland, September 2007
Tsoumakas, L., Friberg, R., Spyromitros-Xiou, E., Kataks, I., Vilcek, J.: MULAN software: java classes for multi-label classification. Available at: http://mlkd.csd.auth.gr/multilabel.html#Software
Wieczorkowska, A., Synak, P., Ras, Z.: Multi-label classification of emotions in music. In: Proceedings of the International Conference on Intelligent Information Processing and Web Mining, pp. 307–315 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Chitra, P.K.A., Balamurugan, S.A.A. (2014). Performance Analysis of Transformation Methods in Multi-Label Classification. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_128
Download citation
DOI: https://doi.org/10.1007/978-81-322-1665-0_128
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
Online ISBN: 978-81-322-1665-0
eBook Packages: EngineeringEngineering (R0)