Open AccessApplication of spatial auto-beta models in statistical classification
Author(s) -
Eglė Zikarienė,
Kęstutis Dučinskas
Publication year - 2021
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.2021.25219
Subject(s) - linear discriminant analysis , pattern recognition (psychology) , artificial intelligence , bayes' theorem , discriminant , discriminant function analysis , classification rule , naive bayes classifier , beta distribution , bayes classifier , mathematics , feature (linguistics) , optimal discriminant analysis , computer science , function (biology) , machine learning , data mining , statistics , bayesian probability , support vector machine , linguistics , philosophy , evolutionary biology , biology
In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.