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master's thesis
Algoritam diferencijalne evolucije za problem odabira značajki
2022. urn:nbn:hr:200:335575
Znaor, Filip
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

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Znaor, F. (2022). Algoritam diferencijalne evolucije za problem odabira značajki (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:335575

Znaor, Filip. "Algoritam diferencijalne evolucije za problem odabira značajki." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2022. https://urn.nsk.hr/urn:nbn:hr:200:335575

Znaor, Filip. "Algoritam diferencijalne evolucije za problem odabira značajki." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2022. https://urn.nsk.hr/urn:nbn:hr:200:335575

Znaor, F. (2022). 'Algoritam diferencijalne evolucije za problem odabira značajki', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 21 March 2023, https://urn.nsk.hr/urn:nbn:hr:200:335575

Znaor F. Algoritam diferencijalne evolucije za problem odabira značajki [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2022 [cited 2023 March 21] Available at: https://urn.nsk.hr/urn:nbn:hr:200:335575

F. Znaor, "Algoritam diferencijalne evolucije za problem odabira značajki", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:200:335575

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Metadata
TitleAlgoritam diferencijalne evolucije za problem odabira značajki
Title (english)A differential evolution algorithm for feature selection
AuthorFilip Znaor
MentorDražen Bajer (mentor)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2022-09-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U radu je opisan problem klasifikacije i odabira značajki (FS) te algoritam diferencijalne evolucije (DE), popularan evolucijski algoritam koji može biti korišten kao omotač za FS. Opisane su tri tehnike diskretizacije rješenja u DE korištenog za FS te oblici pristupa odabira vrijednosti parametara DE. Dan je opis ostvarenog programskog rješenja koje omogućuje izvođenje algoritma DE u svrhu FS s mogućnošću podešavanja parametara DE i korištene tehnike diskretizacije rješenja. Korištenjem ostvarenog programskog rješenja provedena je eksperimentalna analiza na nekoliko često korištenih skupova podataka kojom su uspoređivane tehnike diskretizacije rješenja i pristupi odabira vrijednosti parametara DE u smislu performansi klasifikacije i redukcije broja značajki. Rezultati prikazuju važnost odabira tehnike diskretizacije rješenja, ali i gotovo nepostojeći utjecaj pristupa odabira vrijednosti parametara DE na performanse algoritma
Abstract (english)
The paper describes classification and feature selection (FS), as well as the differential evolution algorithm (DE), a popular evolutionary algorithm that can be used as a wrapper for FS. Three solution discretization techniques used in DE for FS are described, as are types of approaches to parameter value selection in DE. A description is given of the implemented software solution which allows for the execution of the DE algorithm used for FS with the possibility of DE parameter adjustment and the selection of a solution discretization technique. By using the implemented software solution, an experimental analysis was carried out on several frequently used data sets, which compared the solution discretization techniques and approaches to DE parameter value selection based on classification performance and feature reduction. The results show the importance of selecting the solution discretization technique, but also the almost nonexistent influence of the DE parameter value selection approach on the performance of the algorithm
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:335575
Study programmeTitle: Graduate University Study Programme in Computer Engineering
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 conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-10-18 12:40:25