Abstract
In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications such as text classification and medical diagnoses. However, rule-based methods, and especially Learning Classifier Systems (LCS), for tackling such problems have only been sparsely studied. This is the motivation behind our current work that introduces a generalized multi-label rule format and uses it as a guide for further adapting the general Michigan-style LCS framework. The resulting LCS algorithm is thoroughly evaluated and found competitive to other state-of-the-art multi-label classification methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)
Zhang, G.: Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C 30(4), 451–462 (2000)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2010)
Holland, J.: Adaptation. In: Rosen, R., Snell, F.M. (eds.) Progress in Theoretical Biology, vol. 4, pp. 263–293. Academic Press, New York (1976)
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Bernadó-Mansilla, E., Garrell-Guiu, J.: Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolutionary Computation 11(3), 209–238 (2003)
Orriols-Puig, A., Bernadó-Mansilla, E.: Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds.) IWLCS 2006 and IWLCS 2007. LNCS (LNAI), vol. 4998, pp. 96–116. Springer, Heidelberg (2008)
Tzima, F.A., Mitkas, P.A.: Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks. Engineering Applications of Artificial Intelligence 26(2), 818–832 (2013)
Bull, L., Bernadó-Mansilla, E., Holmes, J.H. (eds.): Learning Classifier Systems in Data Mining. SCI, vol. 125. Springer, Heidelberg (2008)
Vallim, R., Duque, T., Goldberg, D., Carvalho, A.: The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results. In: Proceedings of GECCO 2009, pp. 1323–1330. ACM, New York (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)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Random k-labelsets for multilabel classification. IEEE Transactions on Knowledge and Data Engineering 23(7), 1079–1089 (2011)
Zhang, M., Zhou, Z.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)
Schapire, R., Singer, Y.: Boostexter: A boosting- based system for text categorization. Machine learning 39(2), 135–168 (2000)
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Machine Learning 73(2), 185–214 (2008)
Read, J.: Scalable Multi-Label Classification. PhD thesis, University of Waikato, Hamilton, New Zealand (2010)
Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)
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
Allamanis, M., Tzima, F.A., Mitkas, P.A. (2013). Effective Rule-Based Multi-label Classification with Learning Classifier Systems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-37213-1_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37212-4
Online ISBN: 978-3-642-37213-1
eBook Packages: Computer ScienceComputer Science (R0)