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master's thesis
Metode otkrivanja pomaka koncepta u tokovima podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:664624
Grga, Ela
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Grga, E. (2023). Metode otkrivanja pomaka koncepta u tokovima podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:664624

Grga, Ela. "Metode otkrivanja pomaka koncepta u tokovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:664624

Grga, Ela. "Metode otkrivanja pomaka koncepta u tokovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:664624

Grga, E. (2023). 'Metode otkrivanja pomaka koncepta u tokovima podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 24 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:664624

Grga E. Metode otkrivanja pomaka koncepta u tokovima podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 24] Available at: https://urn.nsk.hr/urn:nbn:hr:168:664624

E. Grga, "Metode otkrivanja pomaka koncepta u tokovima podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:664624

Metadata
TitleMetode otkrivanja pomaka koncepta u tokovima podataka
Title (english)Concept drift detection methods in data streams
AuthorEla Grga
MentorAlan Jović (mentor)
Committee memberAlan Jović (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberMarin Golub (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Pojava tokova podataka uvela je novu dimenziju strojnog učenja. U tokovima podataka podaci najčešće pristižu kontinuirano i velikom brzinom. Analiza toka podataka u stvarnom vremenu zahtijeva algoritme i modele koji se mogu prilagoditi promjenjivoj distribuciji podataka, nositi se s pomicanjem koncepta, neuravnoteženosti klasa i stvarati predviđanja u hodu, kako podaci pristižu. Cilj ovog diplomskog rada bio je usporediti dvije metode koje se bave pomakom koncepta u tokovima podataka uz prisustvo neuravnoteženosti klasa. Metoda DDM-OCI u svrhu pomaka koncepta u neuravnoteženom toku podataka prati odziv manjinske klase. Metoda CIDD uravnotežava skup za učenje metodom ADASYN, a prati točnost modela metodom ADWIN. Ispitivanjem metoda nad jednim umjetnim i dva skupa podataka iz stvarnog svijeta, pokazalo se da je metoda DDM-OCI veće točnosti u 9 od 10 ispitnih slučajeva i boljeg odziva manjinske klase u 8 od 10 skupova ispitnih slučajeva u odnosu na metodu CIDD. Međutim, ona to postiže uz cijenu čestog ponovnog učenja modela i s tim velikog trošenja resursa za prikupljanje podataka svaki put kada je potrebno učenje modela
Abstract (english)
The emergence of data streams has introduced a new dimension to machine learning. In data streams, data usually arrives continuously and at high speed. Real-time data stream analysis requires algorithms and models that can adapt to changing data distributions, deal with concept drift, class imbalances, and make predictions on the fly, as data arrives. The aim of this master thesis was to compare two methods that deal with concept drift in data streams in the presence of class imbalance. The DDM-OCI method follows the recall of the minority class for the purpose of detecting concept drift in an imbalanced data stream. The CIDD method balances the learning set using the ADASYN method, and monitors the model accuracy using the ADWIN method. By testing the methods on an artificial and two real-world datasets, it was shown that the DDM-OCI method is more accurate in 9 out of 10 test cases and has a better response of the minority class in 8 out of 10 test cases than the CIDD method. However, this was acheived at the cost of frequent relearning of the model and, thus, there is a large expenditure of data collection resources each time the model needs to be retrained.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:664624
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
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Created on2023-10-11 08:57:08