Abstract
In multi-label learning, each example in the dataset is associated with a set of labels, and the task of the generated classifier is to predict the label set of unseen examples. Feature selection is an important task in machine learning, which aims to find a small number of features that describes the dataset as well as, or even better, than the original set of features does. This can be achieved by removing irrelevant and/or redundant features according to some importance criterion. Although effective feature selection methods to support classification for single-label data are abound, this is not the case for multi-label data. This work proposes two multi-label feature selection methods which use the filter approach. This approach evaluates statistics of the data independently of any particular classifier. To this end, ReliefF, a single-label feature selection method and an adaptation of the Information Gain measure for multi-label data are used to find the features that should be selected. Both methods were experimentally evaluated in ten benchmark datasets, taking into account the reduction in the number of features as well as the quality of the generated classifiers, showing promising results.
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
Preview
Unable to display preview. Download preview PDF.
References
Chen, W., Yan, J., Zhang, B., Chen, Z., Yang, Q.: Document transformation for multi-label feature selection in text categorization. In: IEEE International Conference on Data Mining, pp. 451–456 (2007)
Cherman, E.A., Metz, J., Monard, M.C.: Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems with Applications 39(2), 1647–1655 (2012)
Clare, A., King, R.D.: Knowledge Discovery in Multi-label Phenotype Data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)
Demšar, J.: Algorithms for subsetting attribute values with Relief. Machine Learning 78, 421–428 (2010)
Doquire, G., Verleysen, M.: Feature Selection for Multi-label Classification Problems. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part I. LNCS, vol. 6691, pp. 9–16. Springer, Heidelberg (2011)
Esuli, A., Fagni, T., Sebastiani, F.: Boosting multi-label hierarchical text categorization. Information Retrieval 11(4), 287–313 (2008)
Gu, Q., Li, Z., Han, J.: Correlated multi-label feature selection. In: ACM International Conference on Information and Knowledge Management, pp. 1087–1096 (2011)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman & Hall/CRC (2008)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier Chains for Multi-label Classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 254–269. Springer, Heidelberg (2009)
Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53(1-2), 23–69 (2003)
Spolaôr, N., Monard, M.C., Lee, H.D.: A systematic review to identify feature selection publications in multi-labeled data. ICMC Technical Report No. 374, University of São Paulo, p. 31 (2012)
Spolaôr, N., Cherman, E.A., Monard, M.C.: Using ReliefF for multi-label feature selection. In: Conferencia Latinoamericana de Informática, pp. 960–975 (2011) (in portuguese)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions. In: International Conference on Music Information Retrieval, pp. 1–6 (2008)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 1–19 (2009)
Wei, Q., Yang, Z., Junping, Z., Wang, Y.: Semi-supervised multi-label learning algorithm using dependency among labels. In: International Conference on Machine Learning and Computing, pp. 112–116 (2009)
Zhang, M.L., Peña, J.M., Robles, V.: Feature selection for multi-label naive bayes classification. Information Sciences 179, 3218–3229 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Spolaôr, N., Cherman, E.A., Monard, M.C., Lee, H.D. (2012). Filter Approach Feature Selection Methods to Support Multi-label Learning Based on ReliefF and Information Gain. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-34459-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34458-9
Online ISBN: 978-3-642-34459-6
eBook Packages: Computer ScienceComputer Science (R0)