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undergraduate thesis
Postupci odabira značajki u strojnom učenju
Rijeka: University of Rijeka, Faculty of Engineering, 2020. urn:nbn:hr:190:045294
Ronta, Antonio
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Intelligent Computing Systems

Institutional repository: Repository of the University of Rijeka, Faculty of Engineering

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Ronta, A. (2020). Postupci odabira značajki u strojnom učenju (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:045294

Ronta, Antonio. "Postupci odabira značajki u strojnom učenju." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:045294

Ronta, Antonio. "Postupci odabira značajki u strojnom učenju." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:045294

Ronta, A. (2020). 'Postupci odabira značajki u strojnom učenju', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 05 May 2024, https://urn.nsk.hr/urn:nbn:hr:190:045294

Ronta A. Postupci odabira značajki u strojnom učenju [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2020 [cited 2024 May 05] Available at: https://urn.nsk.hr/urn:nbn:hr:190:045294

A. Ronta, "Postupci odabira značajki u strojnom učenju", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:190:045294

Metadata
TitlePostupci odabira značajki u strojnom učenju
Title (english)Feature selection in machine learning
AuthorAntonio Ronta
MentorGoran Mauša (mentor)
Committee memberViktor Sučić (predsjednik povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Intelligent Computing Systems)
Rijeka
Defense date and country2020-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U današnje vrijeme sve se više podataka prikuplja i pohranjuje te obrađuje raznim tehnikama i metodama koje pruža grana IT industrije strojno učenje, kako bi se izvukle korisne informacije koje bi se kasnije mogle primjeniti u raznim područjima. Radi sve većeg broja značajki koje dajemo modelima na učenje postavlja se pitanje kako efikasno pronaći značajke s najvećim utjecajima te također kako efikasno ukloniti nebitne ili redundantne značajke bez da narušavamo performanse modela kako bi nakraju popravili točnost te smanjili vrijeme učenja. Poddisciplina postupci odabira značajki (engl. feature selection) upravo se bavi ovim problemima te ih pokušava riješiti sa modernim riješenjima. Ovim radom istraženi su filtarski postupci odabira značajki, teorija iza njih, implementacija te u konačnici utjecaji postupaka na performanse modela učenja primjenjenog na problemu predviđanja programskih neispravnosti.
Abstract (english)
Nowadays, more and more data is being collected and stored to be processed by various techniques and methods provided by the IT branch that is machine learning, in order to extract useful information that could later be applied in various fields. Due to the growing number of features we give to learning models, the question is how to efficiently find the features with the greatest impact and also how to effectively remove irrelevant or redundant features without compromising model performance to ultimately improve accuracy and reduce model learning time. The feature selection subdiscipline deals with these problems and is still trying to solve them with modern solutions. In this paper we researched filter methods of feature selection, the theory behind them, implementation, and ultimately the effects of implemented methods on the performance of learning models applied on the problem of predicting software failures.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:045294
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-10-05 06:28:09