Primena mašinskog učenja u problemu nedostajućih podataka pri razvoju prediktivnih modela / Application of machine learning to the problem of missing data in the development of predictive models

Description

<p>Problem nedostajućih podataka je često prisutan prilikom razvoja<br />prediktivnih modela. Umesto uklanjanja podataka koji sadrže<br />vrednosti koje nedostaju mogu se primeniti metode za njihovu<br />imputaciju. Disertacija predlaže metodologiju za pristup analizi<br />uspešnosti imputacija prilikom razvoja prediktivnih modela. Na<br />osnovu iznete metodologije prikazuju se rezultati primene algoritama<br />mašinskog učenja, kao metoda imputacije, prilikom razvoja određenih,<br />konkretnih prediktivnih modela.</p> / <p>The problem of missing data is often present when developing predictive<br />models. Instead of removing data containing missing values, methods for<br />imputation can be applied. The dissertation proposes a methodology for<br />analysis of imputation performance in the development of predictive models.<br />Based on the proposed methodology, results of the application of machine<br />learning algorithms, as an imputation method in the development of specific<br />models, are presented.</p>

Links & Downloads

1. https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija158472080057127.pdf?controlNumber=(BISIS)114270&fileName=158472080057127.pdf&id=15107&source=NDLTD&language=en
2. https://www.cris.uns.ac.rs/record.jsf?recordId=114270&source=NDLTD&language=en

Tags

Additional Fields

Identifer	oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)114270
Date	20 July 2020
Creators	Vrbaški Dunja
Contributors	Kupusinac Aleksandar, Doroslovački Ksenija, Ivetić Dragan, Protić Jelica, Stokić Edita, Sladić Goran
Publisher	Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, University of Novi Sad, Faculty of Technical Sciences at Novi Sad
Source Sets	University of Novi Sad
Language	Serbian
Detected Language	Unknown
Type	PhD thesis