Abstract
Decision Tree Learning is one of the most popular machine learning techniques. A common problem with this approach is the inability to properly manage uncertainty and inconsistency in the underlying datasets. In this work we propose two generalized Decision Tree Learning models based on the notion of Orthopair: the first method allows the induced classifiers to abstain on certain instances, while the second one works with unlabeled outputs, thus enabling semi-supervised learning.
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
References
Ciucci, D.: Orthopairs and granular computing. Granular Comput. 1, 159–170 (2016)
Ellerman, D.: An introduction to logical entropy and its relation to Shannon entropy. Int. J. Semant. Comput. 7(2), 121–145 (2013)
Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, ICDAR 1995, vol. 1, pp. 278–282. IEEE Computer Society (1995)
Liu, Y., Xu, J., Sun, L., Du, L.: Decisions tree learning method based on three-way decisions. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 389–400. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25783-9_35
Nguyen, H.S.: A soft decision tree. In: Kłopotek, M.A., Wierzchoń, S.T., Michalewicz, M. (eds.) Intelligent Information Systems 2002. AINSC, vol. 17, pp. 57–66. Physica, Heidelberg (2002). https://doi.org/10.1007/978-3-7908-1777-5_6
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification. LNS, pp. 149–171. Springer, New York (2003). https://doi.org/10.1007/978-0-387-21579-2_9
Yao, Y.: An outline of a theory of three-way decisions. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 1–17. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32115-3_1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Campagner, A., Ciucci, D. (2018). Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-91476-3_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91475-6
Online ISBN: 978-3-319-91476-3
eBook Packages: Computer ScienceComputer Science (R0)