Abstract
Decision trees are studied in rough set theory [6],[7] and test theory [1], [2], [3] and are used in different areas of applications. The complexity of optimal decision tree (a decision tree with minimal average depth) construction is very high. In the paper some conditions reducing the search are formulated. If these conditions are satisfied, an optimal decision tree for the problem is a result of simple transformation of optimal decision trees for some problems, obtained by decomposition of the initial problem. The decompostion properties are used to show that bounds given in [4] are unimprovable bounds on minimal average depth of decision tree.
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© 1998 Springer-Verlag Berlin Heidelberg
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Chikalov, I. (1998). On Decision Trees with Minimal Average Depth. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_69
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DOI: https://doi.org/10.1007/3-540-69115-4_69
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