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master's thesis
Optimizacija procesa odabira značajki u strojnom učenju
2024. urn:nbn:hr:166:569220
Kumir, Marko
University of Split
Faculty of Science

Cite this document

Kumir, M. (2024). Optimizacija procesa odabira značajki u strojnom učenju (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:569220

Kumir, Marko. "Optimizacija procesa odabira značajki u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:569220

Kumir, Marko. "Optimizacija procesa odabira značajki u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:166:569220

Kumir, M. (2024). 'Optimizacija procesa odabira značajki u strojnom učenju', Master's thesis, University of Split, Faculty of Science, accessed 14 November 2024, https://urn.nsk.hr/urn:nbn:hr:166:569220

Kumir M. Optimizacija procesa odabira značajki u strojnom učenju [Master's thesis]. Split: University of Split, Faculty of Science; 2024 [cited 2024 November 14] Available at: https://urn.nsk.hr/urn:nbn:hr:166:569220

M. Kumir, "Optimizacija procesa odabira značajki u strojnom učenju", Master's thesis, University of Split, Faculty of Science, Split, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:166:569220

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Metadata
TitleOptimizacija procesa odabira značajki u strojnom učenju
Title (english)Optimization of Feature Selection Process in Machine Learning
AuthorMarko Kumir
MentorGoran Zaharija (mentor)
Committee memberGoran Zaharija (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberNika Jerković (član povjerenstva)
GranterUniversity of Split
Faculty of Science
Split
Defense date and country2024-09-25, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Pravilno odabrane značajke igraju ključnu ulogu u poboljšanju performansi modela strojnog učenja, smanjenju složenosti podataka i ubrzavanju procesa treniranja. Nakon detaljnog pregleda literature provedbom PRISMA smjernica, fokus je stavljen na financijsku domenu, posebno na procjenu kreditnog rizika u bankarstvu. U eksperimentalnom dijelu rada testirane su različite filter metode i ugrađene metode, pri čemu je testiranje provedeno na tri kreditna skupa podataka. S ciljem optimizacije procesa, testiranje je izvršeno na 50% najutjecajnijih značajki. Rezultati su pokazali da su RF i XGB modeli, trenirani na reduciranim skupovima podataka, ostvarili visoke performanse u usporedbi s modelima treniranim na inicijalnim skupovima podataka
Abstract (english)
Properly selected features play a crucial role in improving the performance of machine learning models, reducing data complexity, and speeding up the training process. After a detailed literature review conducted using PRISMA guidelines, the focus was placed on the financial domain, specifically on credit risk assessment in banking. In the experimental part of the study, various filter methods and embedded methods were tested, with experiments conducted on three credit datasets. To optimize the process, testing was carried out on 50% of the most relevant features. The results showed that the RF and XGB models, trained on reduced datasets, achieved high performance compared to models trained on the initial datasets.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:569220
Study programmeTitle: Data Science and Engineering
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni/a magistar/magistra podatkovne znanosti i inženjerstva (sveučilišni/a magistar/magistra podatkovne znanosti i inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-10-15 11:54:23