Construction of statistical recognition algorithms based on two-dimensional threshold functions

Abstract

The recognition problem often rises in the classification of multidimensional data and is an integral part of artificial intelligence. Currently, there are many well-known methods and algorithms for solving the recognition problem. However, these methods are mainly aimed at solving such problems where objects are described by a set of independent features. A large number of features describe recognition problems in various fields of human activity. In such problems, the probability of violating the assumption of feature independence is quite high and this determines the relevance of this study.The purpose of this study is to develop a model of recognition algorithms based on the method of making statistical decisions, taking into account the high dimensionality of feature space.A statistical approach to solving the problem of recognition under the conditions of the high dimensionality of feature space is proposed. On the basis of this approach, a model of statistical recognition algorithms based on the construction of two-dimensional threshold rule is proposed. The main advantage of the proposed model is the reduction in the volume of computational operations when recognizing objects in the test set. At that, the reduction of the volume of computational operations is ensured using a number of procedures: selection of representative features; building a two-dimensional threshold rule; determination of a set of reference two-dimensional threshold rules; determination of basic two-dimensional threshold rules and calculation of a probabilistic assessment of the object belonging. This characteristic is very important for real-time recognition systems.The operability of the proposed model of recognition algorithms was investigated in the course of experiments in solving a model problem and the problem of recognizing a person's identity from a signature image. The results of the computational experiment confirm the operability of the proposed approach in solving the recognition problem under conditions when the assumption of feature independence is not satisfied. The results of experimental studies show that the proposed statistical algorithms increase the accuracy of recognition and significantly reduce the volume of computational operations by recognizing an unknown object specified in the space of interrelated features. These algorithms can be used in the compilation of various software systems focused on solving applied recognition problems in the conditions of the high dimensionality of feature space.