Open AccessMathematical Model of Object Classifier based on Bayesian Approach
Author(s) -
Александр Александрович Батенков,
Кирилл Батенков,
Андрей Геннадьевич Богачёв,
Владислав Владимирович Мишин
Publication year - 2020
Publication title -
informatika i avtomatizaciâ/informatika i avtomatizaciâ (print)
Language(s) - English
Resource type - Journals
eISSN - 2713-3206
pISSN - 2713-3192
DOI - 10.15622/ia.2020.19.6.2
Subject(s) - classifier (uml) , mathematics , minimax , artificial intelligence , a priori and a posteriori , mathematical optimization , probabilistic classification , computer science , pattern recognition (psychology) , machine learning , algorithm , naive bayes classifier , support vector machine , philosophy , epistemology
The paper claims that the primary importance in solving the classification problem is to find the conditions for dividing the General complexity into classes, determine the quality of such a bundle, and verify the classifier model. We consider a mathematical model of a non-randomized classifier of features obtained without a teacher, when the number of classes is not set a priori, but only its upper bound is set. The mathematical model is presented in the form of a statement of a minimax conditional extreme task, and it is a problem of searching for the matrix of belonging of objects to a class, and representative (reference) elements within each class. The development of the feature classifier is based on the synthesis of two-dimensional probability density in the coordinate space: classes-objects. Using generalized functions, the probabilistic problem of finding the minimum Bayesian risk is reduced to a deterministic problem on a set of non-randomized classifiers. At the same time, the use of specially introduced constraints fixes non-randomized decision rules and plunges the integer problem of nonlinear programming into a General continuous nonlinear problem. For correct synthesis of the classifier, the dispersion curve of the isotropic sample is necessary. It is necessary to use the total intra-class and inter-class variance to characterize the quality of classification. The classification problem can be interpreted as a particular problem of the theory of catastrophes. Under the conditions of limited initial data, a minimax functional was found that reflects the quality of classification for a quadratic loss function. The developed mathematical model is classified as an integer nonlinear programming problem. The model is given using polynomial constraints to the form of a General problem of nonlinear continuous programming. The necessary conditions for the bundle into classes are found. These conditions can be used as sufficient when testing the hypothesis about the existence of classes.