DETERMINATION OF PRINCIPAL SYMPTOMS IN THE DIVISION OF OBJECTS IN CLASSES
| dc.contributor.author | D.X.Tursunmurotov | |
| dc.date.accessioned | 2025-12-31T13:49:04Z | |
| dc.date.issued | 2021-10-18 | |
| dc.description.abstract | This paper discusses the problem of calculating the weights of quantitative symptoms on the basis of a given criterion. There is no clear distinction between the terms “symptom weight” and “symptom contribution” in terms of content. Sign weight refers to the degree to which that symptom contributes to the classification of objects. A computational experiment was conducted using a sample consisting of several quantitative symptoms, as an example, a collected sample for the detection of diabetes mellitus was taken and the results were described in tables. | |
| dc.format | application/pdf | |
| dc.identifier.uri | https://scholarexpress.net/index.php/wbss/article/view/145 | |
| dc.identifier.uri | https://asianeducationindex.com/handle/123456789/46395 | |
| dc.language.iso | eng | |
| dc.publisher | Scholar Express Journal | |
| dc.relation | https://scholarexpress.net/index.php/wbss/article/view/145/161 | |
| dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.source | World Bulletin of Social Sciences; Vol. 3 (2021): WBSS; 17-19 | |
| dc.source | 2749-361X | |
| dc.subject | Symptom weight | |
| dc.subject | symptom contribution | |
| dc.subject | quantitative symptoms | |
| dc.subject | artificial intelligence | |
| dc.subject | compactness hypothesis | |
| dc.subject | teacher-assisted learning | |
| dc.title | DETERMINATION OF PRINCIPAL SYMPTOMS IN THE DIVISION OF OBJECTS IN CLASSES | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | Peer-reviewed Article |
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