Abstract
In this chapter, we study bi-criteria optimization problems cost versus uncertainty for decision trees and cost versus completeness for inhibitory trees, and consider illustrative examples. The created tools allow us to understand complexity versus accuracy trade-off for decision and inhibitory trees and to choose appropriate trees.
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References
Chikalov, I., Hussain, S., Moshkov, M.: Average depth and number of misclassifications for decision trees. In: Popova-Zeugmann, L. (ed.) 21st International Workshop on Concurrency, Specification and Programming, CS&P 2012, Berlin, Germany, 26–28 Sept 2012. CEUR Workshop Proceedings, vol. 928, pp. 160–169. CEUR-WS.org (2012)
Chikalov, I., Hussain, S., Moshkov, M.: On cost and uncertainty of decision trees. In: Yao, J., Yang, Y., Slowinski, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) Rough Sets and Current Trends in Computing – 8th International Conference, RSCTC 2012, Chengdu, China, 17–20 Aug 2012. Lecture Notes in Computer Science, vol. 7413, pp. 190–197. Springer, Berlin (2012)
Chikalov, I., Hussain, S., Moshkov, M.: Relationships between number of nodes and number of misclassifications for decision trees. In: Yao, J., Yang, Y., Slowinski, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) Rough Sets and Current Trends in Computing – 8th International Conference, RSCTC 2012, Chengdu, China, 17–20 Aug 2012. Lecture Notes in Computer Science, vol. 7413, pp. 212–218. Springer, Berlin (2012)
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Alsolami, F., Azad, M., Chikalov, I., Moshkov, M. (2020). Bi-criteria Optimization Problem for Decision (Inhibitory) Trees: Cost Versus Uncertainty (Completeness). In: Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Intelligent Systems Reference Library, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-12854-8_9
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DOI: https://doi.org/10.1007/978-3-030-12854-8_9
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