On Decision Trees with Minimal Average Depth
Decision trees are studied in rough set theory , and test theory , ,  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  are unimprovable bounds on minimal average depth of decision tree.
KeywordsDecision Tree Average Depth Test Theory Diagnostic Problem Terminal Vertex
Unable to display preview. Download preview PDF.
- 3.Moshkov, M.: Decision Trees. Theory and Applications. Nizhni Novgorod University Publishers, Nizhni Novgorod (1994) (in Russian).Google Scholar
- 5.Moshkov, M., Chikalov, I.: Bounds on average depth of decision trees. Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing, Aachen (1997) 226–230Google Scholar
- 7.Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht (1992) 331–362Google Scholar