Nearest Convex Hull Classification Based on Linear Programming

Autor:	José H. Dulá, A. P. Nemirko
Rok vydání:	2021
Předmět:	Convex hull Statistical classification Linear programming Computer science Feature vector Hull Regular polygon Computer Vision and Pattern Recognition Computational geometry Computer Graphics and Computer-Aided Design Algorithm Classifier (UML)
Zdroj:	Pattern Recognition and Image Analysis. 31:205-211
ISSN:	1555-6212 1054-6618
DOI:	10.1134/s1054661821020139
Popis:	Machine learning methods for automatic classification problems using computational geometry are considered. Classes are defined by convex hulls of sets of points in a multidimensional feature space. The classification algorithms based on the evaluation of the proximity of a test point to the convex hulls of classes are examined. A new method for proximity evaluation based on linear programming is proposed. The corresponding nearest convex hull classifier is described. The results of experimental studies on real problems of medical diagnostics are presented. The comparison of the effectiveness of the proposed classifier with the classifiers of other types has shown a sufficiently high efficiency of the proposed method for proximity evaluation based on linear programming.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::90a1e50f6877065bb6427b8fe3c8cf52 https://doi.org/10.1134/s1054661821020139 Zobrazit plný text záznamu Full text from SpringerLink