The object of research is classification trees. The subject of research is methods, algorithms, and schemes for constructing classification trees. The aim of this work is to build an effective method (scheme) for synthesizing classification tree models based on a group assessment of the importance of discrete features within a branched attribute selection.
The general problem of constructing logical recognition and classification trees is considered. The object of this study is logical classification trees. The subject of the research is current methods and algorithms for constructing logical classification trees. The aim of the work is to create a simple and effective method for constructing recognition models based on classification trees for training samples of discrete information, which is characterized by elementary features in the structure of synthesized logical classification trees.
The problem of convergence of the procedure for synthesizing classifier schemes in the methods of logical and algorithmic classification trees is considered. An upper estimate of the complexity of the algorithm tree scheme is proposed in the problem of approximating an array of real data with a set of generalized features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree.