Geodesic
The problem of the formation of generalized concepts when working with uncertain attributes
Nečetkie sistemy i mâgkie vyčisleniâ, Tome 12 (2017) no. 2, pp. 151-158.

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This paper considers the problem of data generalization, taking into account uncertain information in attributes. Algorithms capable of operating with uncertain data are considered. We suggest methods that improve UDT algorithm. The results of experiments on the application of the proposed algorithms are given.
Keywords: generalization problem, undefined numeric attribute, decision tree. 
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I. I. Astakhova; V. N. Vagin; M. V. Fomina. The problem of the formation of generalized concepts when working with uncertain attributes. Nečetkie sistemy i mâgkie vyčisleniâ, Tome 12 (2017) no. 2, pp. 151-158. http://geodesic.mathdoc.fr/item/FSSC_2017_12_2_a5/

[1] Vagin V. N., Golovina E. Yu., Zagoryanskaya A. A., Fomina M. V., A Reliable and Plausible Derivation in Intellectual Systems, eds. V. N. Vagin, D. A. Pospelov, Fizmatlit Publ., Moscow, 2008, 712 pp. (in Russian)

[2] Merz C. J., Murphy P. M., UCI Repository of Machine Learning Datasets, Information and Computer Science University of California, 1998, http://archive.ics.uci.edu/ml/

[3] Quinlan J. R., Induction of Decision Trees Machine Learning 1, 1986, 81-106

[4] Quinlan J. R., “Improved Use of Continuous Attributes in C 4.5”, Journal of Artifical Intelligence Research, 4 (1996), 77-90 | DOI | Zbl

[5] Qin B., Xia Y., Prabhakar S., Tu Y., “A rule-based classification algorithm for uncertain data”, The Workshop on Management and Mining Of Uncertain Data (MOUND), 2009

[6] Tsang S., Kao B., Yip K. Y., Ho W. S., Lee S. D., “Decision Trees for Uncertain Data”, IEEE 25th International Conference on Data Engineering, ICDE '09 (Shanghai, China, 2009), 441-444 | DOI

[7] Xia Y., Xi B., “Conceptual clustering categorical data with uncertainty”, Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007 (Patras, Greece, 2007), 329-336 | DOI