Abstract
A comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it.
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References
Alkhalid, A., Chikalov, I., Moshkov, M.: On algorithm for building of optimal α-decision trees. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS(LNAI), vol. 6086, pp. 438–445. Springer, Heidelberg (2010)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth & Brooks (1984)
Chegis, I.A., Yablonskii, S.V.: Logical methods of electric circuit control. Trudy Mian SSSR 51, 270–360 (1958) (in Russian)
Chikalov, I., Moshkov, M., Zelentsova, M.: On optimization of decision trees. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 18–36. Springer, Heidelberg (2005)
Hyafil, L., Rivest, R.: Constructing optimal binary decision trees is NP-complete. Information Processing Letters 5, 15–17 (1976)
Moret, B.E., Thomason, R.C., Gonzalez, M.: The activity of a variable and its relation to decision trees. ACM Transactions on Programming Languages and Systems 2, 580–595 (1980)
Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
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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Alkhalid, A., Chikalov, I., Moshkov, M. (2011). Comparison of Greedy Algorithms for α-Decision Tree Construction. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_25
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DOI: https://doi.org/10.1007/978-3-642-24425-4_25
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