Abstract
Decision trees are considered to be among the best classifiers. In this work we use decision trees and its families to the problem of imbalanced data recognition. Considered are aspects of recognition without rejection and with rejection: it is assumed that all recognized elements belong to desired classes in the first case and that some of them are outside of such classes and are not known at classifier’s training stage. The facets of imbalanced data and recognition with rejection affect different real world problems. In this paper we discuss results of experiment of imbalanced data recognition on the case study of music notation symbols. Decision trees and three methods of joining decision trees (simple voting, bagging and random forest) are studied. These methods are used for recognition without and with rejection.
Chapter PDF
Similar content being viewed by others
Keywords
References
Abe, N., Zadrozny, B., Langford, J.: An Iterative Method for Multi-Class Cost-Sensitive Learning. In: Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 3–11 (2004)
Breiman, L.: Bagging predictors. Machine Learning 26(2), 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, Inc., New York (2001)
Garcia, V., Sanchez, J.S., Mollineda, R.A., Alejo, R., Sotoca, J.M.: The class imbalance problem in pattern recognition and learning. In: II Congreso Espanol de Informatica, pp. 283–291 (2007)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284 (2009)
Homenda, W.: Optical Music Recognition: the Case Study of Pattern Recognition. In: Computer Recognition Systems, pp. 835–842. Springer (2005)
Homenda, W., Luckner, M., Pedrycz, W.: Classification with rejection: concepts and formal evaluations. In: Andrzej, M.J. (ed.) Proceedings of KICSS 2013, pp. 161–172. Progress & Business Publishers, Krakow (2013)
Homenda, W., Lesinski, W.: Optical Music Recognition: Case of Pattern recognition with Undesirable and Garbage Symbols. In: Choras, R., et al. (eds.) Image Processing and Communications Challenges, pp. 120–127. Exit, Warsaw (2009)
Lesinski, W., Jastrzebska, A.: Optical Music Recognition as the Case of Imbalanced Pattern Recognition: a Study of Single Classifiers. In: Skulimowski, A.M.J. (ed.) Proceedings of KICSS 2013, pp. 267–278. Progress & Business Publishers, Krakow (2013)
Lesinski, W., Jastrzebska, A.: Optical Music Recognition as the Case of Imbalanced Pattern Recognition: A Study of Complex Classifiers. In: Swiątek, J., Grzech, A., Swiątek, P., Tomczak, J.M., et al. (eds.) Advances in Systems Science. Lesinski W., Jastrzebska A, vol. 240, pp. 325–335. Springer, Heidelberg (2014)
Koronacki, J., Cwik, J.: Statystyczne systemy uczace sie. Exit, Warszawa (2008) (in Polish)
Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. John Wiley & Sons (2004)
Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R.S., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of-the-art and open issues. International Journal of Multimedia Information Retrieval 1, 173–190 (2012)
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Zhou, Z.H., Liu, X.Y.: On Multi-Class Cost-Sensitive Learning. Computational Intelligence 26, 232–257 (2010)
Breaking accessibility barriers in information society. Braille Score - design and implementation of a computer program for processing music information for blind people, the research project no N R02 0019 06/2009 supported by by The National Center for Research and Development, Poland (2009-2012)
Cognitive maps with imperfect information as a tool of automatic data understanding. Ideas, methods, applications, the research project no 2011/01/B/ST6/06478 supported by the National Science Center, Poland (2011-2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Homenda, W., Lesinski, W. (2014). Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection. In: Saeed, K., Snášel, V. (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science, vol 8838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45237-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-662-45237-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45236-3
Online ISBN: 978-3-662-45237-0
eBook Packages: Computer ScienceComputer Science (R0)