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master's thesis
Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama
Zagreb: University of Zagreb, Faculty of Economics and Business, 2023. urn:nbn:hr:148:867597
Korman, Mateo
University of Zagreb
Faculty of Economics and Business
Department of Informatics

Institutional repository: REPEFZG

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Korman, M. (2023). Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama (Master's thesis). Zagreb: University of Zagreb, Faculty of Economics and Business. Retrieved from https://urn.nsk.hr/urn:nbn:hr:148:867597

Korman, Mateo. "Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2023. https://urn.nsk.hr/urn:nbn:hr:148:867597

Korman, Mateo. "Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2023. https://urn.nsk.hr/urn:nbn:hr:148:867597

Korman, M. (2023). 'Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama', Master's thesis, University of Zagreb, Faculty of Economics and Business, accessed 25 June 2024, https://urn.nsk.hr/urn:nbn:hr:148:867597

Korman M. Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama [Master's thesis]. Zagreb: University of Zagreb, Faculty of Economics and Business; 2023 [cited 2024 June 25] Available at: https://urn.nsk.hr/urn:nbn:hr:148:867597

M. Korman, "Ispitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama", Master's thesis, University of Zagreb, Faculty of Economics and Business, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:148:867597

Metadata
TitleIspitivanje performansi genetskog algoritma za klasifikaciju podataka u poslovnim primjenama
Title (english)Testing the performance of a genetic algorithm for data classification in business applications
AuthorMateo Korman
MentorMirjana Pejić Bach (mentor)
Committee memberKatarina Ćurko (član povjerenstva)
Committee memberNikola Vlahović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Economics and Business
(Department of Informatics)
Zagreb
Defense date and country2023-09-30, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
U svijetu u kojem danas živimo, kada se svaki dan generiraju ogromne količine podataka, poduzeća su primorana naučiti kako iz njih iskoristiti maksimum. Jedan od načina su korištenje raznih metoda otkrivanja znanja iz podataka. Pristupom internetu, svima su dostupni razni alati i već napisani dijelovi programskog koda koji se koriste u algoritmima te je tako pristup otkrivanju znanja iz podataka uvelike olakšan. Jedan od takvih alata s knjižnicom mnoštva algoritama je Weka. Glavni fokus u ovom radu je klasifikacija podataka pomoću algoritama šuma, odnosno usporediti algoritme šuma s algoritmom temeljenim na genetskom algoritmu. Uz pomoć Weka-e prezentirani su rezultati četiri klasifikacijskih algoritama nad pet setova podataka iz različitih poslovnih područja te donesen zaključak o performansama genetskog algoritma.
Abstract (english)
In the world and time we live in today, when huge amounts of data are generated every day, companies are forced to learn how to make the most of it. One of the ways is the use of various methods of discovering knowledge from data. With access to the Internet, various tools and already written parts of program code that are used in algorithms are available to everyone, and thus access to discovering knowledge from data is greatly facilitated. One such tool with a library of many algorithms is Weka. The main focus of this paper is data classification using forest algorithms, respectively to compare forest algorithms with an algorithm based on a genetic algorithm. With the help of Weka, the results of four classification algorithms were presented over five sets of data from different business areas, and in the and conclusion was delivered in order to evaluate performance of genetic algorithm.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:148:867597
Study programmeTitle: Electronic business in private and public sector
Study programme type: professional
Study level: specialist graduate
Academic / professional title: stručni/a specijalist/specijalistikca za elektroničko poslovanje u privatnom i javnom sektoru (stručni/a specijalist/specijalistikca za elektroničko poslovanje u privatnom i javnom sektoru)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) icon
Created on2023-10-02 16:49:46