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master's thesis
Strojno učenje na nebalansiranim skupovima podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:389445
Grgić, Silvija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Grgić, S. (2021). Strojno učenje na nebalansiranim skupovima podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:389445

Grgić, Silvija. "Strojno učenje na nebalansiranim skupovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:389445

Grgić, Silvija. "Strojno učenje na nebalansiranim skupovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:389445

Grgić, S. (2021). 'Strojno učenje na nebalansiranim skupovima podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 03 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:389445

Grgić S. Strojno učenje na nebalansiranim skupovima podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:389445

S. Grgić, "Strojno učenje na nebalansiranim skupovima podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:389445

Metadata
TitleStrojno učenje na nebalansiranim skupovima podataka
Title (english)Machine learning on imbalanced datasets
AuthorSilvija Grgić
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberMario Brčić (član povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Skupovi podataka koje koriste algoritmi strojnog učenja mogu biti nebalansirani i ta karakteristika može utjecati na rezultate tih algoritama. U sklopu ovog diplomskog rada opisani su pojedini algoritmi strojnog učenja i dva nebalansirana skupa podataka. Prikazani su rezultati algoritama strojnog učenja i neuronske mreže nad tim nebalansiranim skupovima podataka ali i rezultati istih algoritama i neuronske mreže nad istim skupovima podataka samo uz primjenu SMOTE i NearMiss metoda za balansiranje nebalansiranih skupova podataka.
Abstract (english)
Data sets used by machine learning algorithms can be unbalanced and this characteristic can affect the results of these algorithms. As a part of this thesis, few machine learning algorithms and two unbalanced data sets are described. The results of machine learning and neural network algorithms over these unbalanced data sets are presented, but also the results of the same algorithms and neural network over the same data sets only with the application of SMOTE and NearMiss methods for balancing unbalanced data sets.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:389445
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 12:21:46