Greedy Algorithm of Decision Tree Construction for Real Data Tables

In the paper a greedy algorithm for minimization of decision tree depth is described and bounds on the algorithm precision are considered. This algorithm is applicable to data tables with both discrete and continuous variables which can have missing values. Under some natural assumptions on the class NP and on the class of considered tables, the algorithm is, apparently, close to best approximate polynomial algorithms for minimization of decision tree depth.