Abstract
The ideas underlying a series of the authors’ studies dealing with the design of classification algorithms based on full decision trees are further developed. It is shown that the decision tree construction under consideration takes into account all the features satisfying a branching criterion. Full decision trees with an entropy branching criterion are studied as applied to precedent-based pattern recognition problems with real-valued data. Recognition procedures are constructed for solving problems with incomplete data (gaps in the feature descriptions of the objects) in the case when the learning objects are nonuniformly distributed over the classes. The authors’ basic results previously obtained in this area are overviewed.
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