Abstract
The straightforward approach to multi-label classification is based on decomposition, which essentially treats all labels independently and ignores interactions between labels. We propose to enhance multi-label classifiers with features constructed from local patterns representing explicitly such interdependencies. An Exceptional Model Mining instance is employed to find local patterns representing parts of the data where the conditional dependence relations between the labels are exceptional. We construct binary features from these patterns that can be interpreted as partial solutions to local complexities in the data. These features are then used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of decompositive multi-label learning techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining Multi-label Data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2010)
Paine, R.T.: Food Web Complexity and Species Diversity. The American Naturalist 100(910), 65–75 (1966)
Fürnkranz, J., Knobbe, A. (eds.): Special Issue: Global Modeling Using Local Patterns. Data Mining and Knowledge Discovery Journal 20 (1) (2010)
Leman, D., Feelders, A., Knobbe, A.: Exceptional Model Mining. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 1–16. Springer, Heidelberg (2008)
Duivesteijn, W., Knobbe, A., Feelders, A., van Leeuwen, M.: Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In: Proc. ICDM, pp. 158–167 (2010)
Duivesteijn, W., Loza Mencía, E., Fürnkranz, J., Knobbe, A.: Multi-label LeGo — Enhancing Multi-label Classifiers with Local Patterns. Technical Report, Technische Universität Darmstadt, TUD-KE-2012-02 (2012), http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2012-02.pdf
Knobbe, A., Valkonet, J.: Building Classifiers from Pattern Teams. In: From Local Patterns to Global Models: Proc. ECML PKDD 2009 Workshop, Slovenia (2009)
Sulzmann, J.-N., Fürnkranz, J.: A Comparison of Techniques for Selecting and Combining Class Association Rules. In: From Local Patterns to Global Models: Proc. ECML PKDD 2008 Workshop, Belgium (2008)
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Machine Learning 76(2-3), 211–225 (2009)
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)
Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel Classification via Calibrated Label Ranking. Machine Learning 73(2), 133–153 (2008)
Shapiro, L.G., Haralick, R.M.: A Metric for Comparing Relational Descriptions. IEEE Trans. Pattern Anal. Mach. Intell. 7, 90–94 (1985)
Verma, T., Pearl, J.: Equivalence and Synthesis of Causal Models. In: Proc. UAI, pp. 255–270 (1990)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)
van Leeuwen, M., Knobbe, A.: Non-redundant Subgroup Discovery in Large and Complex Data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 459–474. Springer, Heidelberg (2011)
Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the Stratification of Multi-label Data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 145–158. Springer, Heidelberg (2011)
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
Duivesteijn, W., Loza Mencía, E., Fürnkranz, J., Knobbe, A. (2012). Multi-label LeGo — Enhancing Multi-label Classifiers with Local Patterns. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-34156-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34155-7
Online ISBN: 978-3-642-34156-4
eBook Packages: Computer ScienceComputer Science (R0)