Abstract
In the paper algorithms are considered which allow to consecutively optimize decision trees for decision tables with many-valued decisions relatively different complexity measures such as number of nodes, weighted depth, average weighted depth, etc. For decision tables over an arbitrary infinite restricted information system [5] these algorithms have (at least for the three mentioned measures) polynomial time complexity depending on the length of table description. For decision tables over one of such information systems experimental results of decision tree optimization are described.
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Chikalov, I.V.: Algorithm for constructing of decision trees with minimal number of nodes. In: Proceedings of the Second International Conference on Rough Sets and Current Trends in Computing, Banff, Canada, pp. 107–111 (2000)
Chikalov, I.V.: Algorithm for constructing of decision trees with minimal average depth. In: Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, Madrid, Spain, vol. 1, pp. 376–379 (2000)
Chlebus, B.S., Nguyen, S.H.: On finding optimal discretizations for two attributes. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 537–544. Springer, Heidelberg (1998)
Moshkov, M.J., Chikalov, I.V.: On effective algorithms for construction of decision trees. In: Proceedings of the Twelfth International Conference Problems of Theoretical Cybernetics, Part 2. Nizhny Novgorod, Russia, p. 165 (1999) (in Russian)
Moshkov, M.J., Chikalov, I.V.: On algorithm for constructing of decision trees with minimal depth. Fundamenta Informaticae 41(3), 295–299 (2000)
Moshkov, M.J., Chikalov, I.V.: Consecutive optimization of decision trees concerning various complexity measures. Fundamenta Informaticae 61(2), 87–96 (2004)
Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
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Chikalov, I.V., Moshkov, M.J., Zelentsova, M.S. (2005). On Optimization of Decision Trees. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets IV. Lecture Notes in Computer Science, vol 3700. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11574798_2
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DOI: https://doi.org/10.1007/11574798_2
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