LIMITED METHOD FOR THE CASE OF ALGORITHMIC CLASSIFICATION TREE
Keywords:Algorithmic classification tree, image recognition, classification, classification algorithm, branching criterion, limited method.
Context. The general problem of constructing the algorithmic recognition (classification) trees on the basis of a limited method in the artificial intelligence theory has been considered. The object of the present study is a concept of classification tree (a limited method-based algorithmic classification tree). The relevant methods, algorithms and schemes (a limited method) of constructing the algorithmic classification trees are the subject of this study.
Objective. The goal of this work is to develop a simple and efficient limited method of constructing the tree-like recognition and classification models on the basis of the algorithmic classification trees for training selection of a large-volume discrete information that is characterized by a structure of classification trees obtained from independent recognition algorithms assessed in accordance with their general efficiency calculation functional for a wide class of applied tasks.
Method. A limited method of constructing the algorithmic classification tree is suggested that constructs a tree-like structure for the preset initial training selection (an ACT model) consisting of a set of autonomous classification/recognition algorithms assessed at each ACT construction step (stage) in accordance with the initial classification. In other words, the limited method of constructing the algorithmic classification tree is suggested, and its idea is a step-by-step approximation of the arbitrary volume/structure selection by a set of independent classification/recognition algorithms. This method provides formation of a current algorithmic tree vertex (node, the generalized ACT attribute) with the selection of the most efficient (high-quality) autonomous classification algorithms from the initial set and construction completion of only those ACT structure paths, where the largest number of classification errors occurs. Such approach at constructing the resultant classification tree (the ACT model) allows the tree size and complexity (i.e. the total number of transitions, structure vertices and layers) to be reduced considerably, the quality of the next analysis (interpretability) and the possibility of decomposition to be increased as well as the ACT structures to be built given the limited hardware resources. The above limited method of constructing the algorithmic classification tree enables one to construct diverse tree-like recognition models with a preset accuracy for a wide class of the artificial intelligence theory tasks.
Results. The limited method of constructing the algorithmic classification tree developed and presented in this work has software realization and was investigated and compared to the logical classification tree methods (on the basis of elementary attribute set selection) and the algorithmic tree classification methods (first and second-type ones) when solving the task of real geological data recognition.
Conclusions. The experiments carried out in the present work have proved the performance capabilities of the software suggested and demonstrate the possibility of its promising utilization for the solution of a wide spectrum of applied recognition/classification problems. The outlook of further studies and approbations may be related to the creation of methods of other-type algorithmic classification trees that introduce a stopping criterion for the procedure of a tree model in accordance with the structure depth, optimization of its software realizations and to the experimental studies of this method for a wider circle of practical tasks.
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