1. Інституційний репозитарій Чорноморського національного університету імені Петра Могили (irPMBSNU)
  2. Публікації у наукометричних базах
  3. Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus
Please use this identifier to cite or link to this item: https://dspace.chmnu.edu.ua/jspui/handle/123456789/1436
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dc.contributor.authorChuiko, G.-
dc.contributor.authorDvornik, O.-
dc.contributor.authorDarnapuk, Y.-
dc.contributor.authorHoncharov, D.-
dc.contributor.authorKrainyk, Y.-
dc.contributor.authorYaremchuk, O.-
dc.date.accessioned2023-12-22T08:00:40Z-
dc.date.available2023-12-22T08:00:40Z-
dc.date.issued2023-
dc.identifier.isbn979-835038309-6-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179502539&doi=10.1109%2fELIT61488.2023.10310922&partnerID=40-
dc.identifier.urihttps://ieeexplore.ieee.org/document/10310922-
dc.identifier.urihttps://dspace.chmnu.edu.ua/jspui/handle/123456789/1436-
dc.descriptionChuiko, G., Dvornik, O., Darnapuk, Y., Honcharov, D., Krainyk, Y., & Yaremchuk, O. (2023). Attribute Selection, Outliers Impact Study and Visualization within Breast Cancer Detection. 2023 IEEE 13th International Conference on Electronics and Information Technologies, ELIT 2023 - Proceedings, (pp. 1-5). Lviv, Ukraine : IEEE. doi: 10.1109/ELIT61488.2023.10310922uk_UA
dc.description.sponsorshipClassification of mammography data into two types of breast tumors, benign or malignant, is an effective screening tool and the primary way of diagnosis and decision-making. This report aims to opt for the most relevant attributes of the well-known Wisconsin Breast Cancer Diagnostic Data Set to reduce its size at first. The reduction was performed initially by ranking attributes and finally by "decision tree" analysis. The clipped data set had only six attributes, against 31 in the initial one. The five most relevant attributes were the following: "perimeter_worst," "area_worst," "concave points_worst," "texture_mean," and "concave points_mean." If possible, It should be done without losing classification potency. Over and above, our extra goal was to find classifiers that provide acceptable performance while allowing visualization of the results in a way accessible to clinicians. Here, we mean various visualization tools in the Machine learning framework: "decision trees," association rules, attribute ranking, and so forth, to improve breast cancer diagnosis.uk_UA
dc.language.isoenuk_UA
dc.publisherInstitute of Electrical and Electronics Engineers Inc.uk_UA
dc.subjectAttribute Selectionuk_UA
dc.subjectBreast Cancer Diagnosticsuk_UA
dc.subjectDecision-Treesuk_UA
dc.subjectMachine Learninguk_UA
dc.subjectoutliersuk_UA
dc.titleAttribute Selection, Outliers Impact Study and Visualization within Breast Cancer Detectionuk_UA
dc.typeThesisuk_UA
Appears in Collections:Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus

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