Abstract
Multi-label classification has been increasingly recognized since it can classify objects into multiple classes, simultaneously. However, its effectiveness might be sacrificed due to high dimensionality problem in feature space and sparseness problem in label space. To address these issues, this paper proposes a Two-Stage Dual Space Reduction (2SDSR) framework that transforms both feature space and label space into the lower-dimensional spaces. In our framework, the label space is transformed into reduced label space and then supervised dimensionality reduction method is applied to find a small number of features that maximizing dependency between features and that reduced labels. Using these reduced features and labels, a set of classification models are built. In this framework, we employ two well-known feature reduction methods such as MDDM and CCA, and two widely used label reduction methods i.e., PLST and BMD. However, it is possible to apply various dimensionality reduction methods into the framework. By a set of experiments on five real world datasets, the results indicated that our proposed framework can improve the classification performance, compared to the traditional dimensionality reduction approaches which reduce feature space or label space only.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fan, R.E., Lin, C.J.: A Study on Threshold Selection for Multi-label Classification. National Taiwan University (2007)
Golub, G., Reinsch, C.: Singular value decomposition and least squares solutions. Numerische Mathematik 14, 403–420 (1970)
Gretton, A., Bousquet, O., Smola, A.J., Schölkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63–77. Springer, Heidelberg (2005)
Hotelling, H.: The most predictable criterion. Journal of Educational Psychology 26, 139–142 (1935)
Hsu, D., Kakade, S., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. Proceedings of the Advances in Neural Information Processing Systems 22, 772–780 (2009)
Miettinen, P.: The boolean column and column-row matrix decompositions. Data Mining and Knowledge Discovery 17(1), 39–56 (2008)
Pacharawongsakda, E., Theeramunkong, T.: Towards more efficient multi-label classification using dependent and independent dual space reduction. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 383–394. Springer, Heidelberg (2012)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Machine Learning, 1–27 (2011)
Schapire, R., Singer, Y.: Boostexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135–168 (2000)
Tai, F., Lin, H.T.: Multi-label classification with principle label space transformation. In: Proceedings of the 2nd International Workshop on Learning from Multi-Label Data (MLD 2010), pp. 45–52 (2010)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data, 2nd edn. Data Mining and Knowledge Discovery Handbook. Springer (2010)
Wicker, J., Pfahringer, B., Kramer, S.: Multi-label classification using boolean matrix decomposition. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186. Springer, Heidelberg (2012)
Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 258–265 (2005)
Zhang, Y., Zhou, Z.H.: Multilabel dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data (TKDD) 4(3), 1–21 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pacharawongsakda, E., Theeramunkong, T. (2013). A Two-Stage Dual Space Reduction Framework for Multi-label Classification. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-40319-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40318-7
Online ISBN: 978-3-642-40319-4
eBook Packages: Computer ScienceComputer Science (R0)