Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection
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.
Keywordspattern recognition decision tree bagging random forest optical music recognition imbalanced data
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- 1.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)Google Scholar
- 5.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)Google Scholar
- 7.Homenda, W.: Optical Music Recognition: the Case Study of Pattern Recognition. In: Computer Recognition Systems, pp. 835–842. Springer (2005)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 10.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)Google Scholar
- 11.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)CrossRefGoogle Scholar
- 12.Koronacki, J., Cwik, J.: Statystyczne systemy uczace sie. Exit, Warszawa (2008) (in Polish)Google Scholar
- 13.Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. John Wiley & Sons (2004)Google Scholar
- 15.Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)Google Scholar
- 18.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)Google Scholar
- 19.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)Google Scholar