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Title:Metoda podpornih vektorjev v detekciji goljufij
Authors:Krajnčič, Manja (Author)
Bokal, Drago (Mentor) More about this mentor... New window
Žnidaršič, Anja (Co-mentor)
Files:.pdf MAG_Krajncic_Manja_2019.pdf (1,31 MB)
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:Magistrsko delo obravnava problem odkrivanja goljufij za izbrani scenarij. Scenarij nam predstavlja eno obliko goljufanja, ki jo želimo razkriti z uporabo ustrezne metode. Kljub temu, da je za odkrivanje goljufij razvitih veliko metod, pa vse niso ustrezne. Metode, ki se v prvi vrsti delijo na nadzorovane in nenadzorovane, ne odkrijejo vseh vrst goljufij, zato je zelo pomembno, da ustvarimo več scenarijev in prilagodimo metode glede na naš nabor podatkov, s tem pa pokrijemo večjo množico možnih goljufov. Za scenarij si izberemo goljufanje gostincev, nad katerim razvijemo novo metodo za odkrivanje transakcijskih goljufij. Rezultate primerjamo tudi z rezultati, ki jih nad isto množico podatkov dobimo pri uporabi metode podpornih vektorjev enega razreda. Glavni rezultat magistrske naloge nam predstavlja kombinacijo uporabe dveh metod za rangiranje gostincev od najbolj do najmanj sumljivih.
Keywords:odkrivanje goljufij, subvencionirana študentske prehrana, metoda FSRO, metoda podpornih vektorjev enega razreda
Year of publishing:2019
Publisher:[M. Krajnčič]
Source:Maribor
UDC:519.87(043.2)
COBISS_ID:24717832 Link is opened in a new window
NUK URN:URN:SI:UM:DK:XMIEPGAO
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:101
Downloads:13
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:FNM
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Secondary language

Language:English
Title:Support vector machine and fraud detection
Abstract:The master thesis deals with fraud detection problem for a specific scenario. The scenario represents one type of fraud that we want to detect with a proper method. There are a lot of different methods for fraud detection, but not all of them are appropriate. Methods that are classified as supervised and unsupervised, do not detect all kinds of fraud, so it is very important to create multiple scenarios in our data set to cover diverse possibilities for fraud. For our scenario, we assume that only provider can commit fraud. We then develop a new method for a fraud detection in telecommunications and compare the results with results from method one-class support vector machine. The main result of master thesis represents a combination of using these two methods for ranking providers from most to least suspicious.
Keywords:fraud detection, subsidized student meals, FSRO method, one-class support vector machine


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