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Title:RAZVOJ IN IMPLEMENTACIJA KLASIFIKATORJA V OKOLJU WEKA
Authors:ID Mažgon, Lovro (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UNI_Mazgon_Lovro_2013.pdf (1,32 MB)
MD5: 9EC5BEE192C0AB228F77EABDCBDDA93F
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo se nanaša na področje odkrivanja znanja iz podatkov, še natančneje pa opisuje klasifikacijo, priznane klasifikatorje ter mere za določanje kakovosti klasifikacije. V delu smo predstavili razvoj lastnega algoritma za klasifikacijo, ki s pomočjo izračunov oddaljenosti od učnih primerkov določi razred neznanemu primerku. Podali smo matematično definicijo algoritma ter opis implementacije v okolju Weka, s pomočjo katerega smo preizkusili uspešnost klasifikacije na dveh realnih medicinskih primerih. Dobljeni rezultati nakazujejo, da razviti algoritem razmeroma uspešno klasificira primerke in se lahko primerja s priznanimi klasifikatorji.
Keywords:strojno učenje, podatkovno rudarjenje, klasifikacija, Weka
Place of publishing:Maribor
Publisher:[L. Mažgon]
Year of publishing:2013
PID:20.500.12556/DKUM-41976 New window
UDC:004.6(043.2)
COBISS.SI-ID:17470486 New window
NUK URN:URN:SI:UM:DK:RVT8QRTP
Publication date in DKUM:18.09.2013
Views:3083
Downloads:267
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:DESIGN AND IMPLEMENTATION OF A CLASSIFIER IN WEKA
Abstract:The thesis addresses the area of knowledge discovery from data, and even more specifically, it describesthe classification, the recognized classifiers and the metrics for evaluating classifier performance. We have presented the development of our own algorithm for classification which determines an unknown sample’s class with the help of distance calculations from the learning samples. The mathematical definition of the algorithm has been provided, as well as the description of its implementation in the Weka environment, through which we have tested the performance of the classifier on two real medical cases. The results indicate that the algorithm classifies samples relatively successfully and it can be compared with recognized classifiers.
Keywords:machine learning, data mining, classification, Weka


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