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Title:OPTIMIZACIJA Z ROJEM DELCEV ZA IZBIRO ATRIBUTOV PRI KLASIFIKACIJI
Authors:ID Brezočnik, Lucija (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf MAG_Brezocnik_Lucija_2016.pdf (10,64 MB)
MD5: 24E9F4AF0CD76848543CC683D5ADA9FA
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo razvili metodo FS-BPSO, ki združuje postopek izbire atributov z algoritmom optimizacije z rojem delcev. Glavni namen te metode je njena uporabnost pri reševanju naslednjega dobro znanega problema. Ko so v podatkovni množici primerki z ogromnim številom atributov, je med njimi težko najti tiste, ki so najbolj informativni oziroma reprezentativni. Tega problema smo se lotili s predlaganim hibridnim algoritmom binarne optimizacije z rojem delcev v kombinaciji s klasifikacijskimi metodami C4.5, Naive Bayes in SVM v cenitveni funkciji za izbiro informativnih atributov. Dobljeni rezultati so statistično analizirani in razkrivajo, da predlagani hibridni algoritem prekaša znane klasifikacijske metode C4.5, Naive Bayes in SVM.
Keywords:računalniška inteligenca, optimizacija z rojem delcev, metoda izbire atributov, klasifikacija
Place of publishing:[Maribor
Publisher:L. Brezočnik
Year of publishing:2016
PID:20.500.12556/DKUM-61656 New window
UDC:004.89(043.2)
COBISS.SI-ID:20148502 New window
NUK URN:URN:SI:UM:DK:NOXHVA9K
Publication date in DKUM:06.09.2016
Views:1736
Downloads:275
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
BREZOČNIK, Lucija, 2016, OPTIMIZACIJA Z ROJEM DELCEV ZA IZBIRO ATRIBUTOV PRI KLASIFIKACIJI [online]. Master’s thesis. Maribor : L. Brezočnik. [Accessed 26 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=61656
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Secondary language

Language:English
Title:PARTICLE SWARM OPTIMIZATION IN FEATURE SELECTION FOR CLASSIFICATION
Abstract:In this master's thesis, we have developed an FS-BPSO method that joins a feature selection procedure with a particle swarm optimization algorithm. The main purpose of this method is its usability in addressing the following well-known problem: When there are instances with a huge number of attributes in a data set, it is hard to select the most representative ones among them. In order to cope with this problem, we propose the use of a hybrid binary particle swarm optimization algorithm combined with C4.5, Naive Bayes, and SVM as the classifiers in the fitness function for the selection of informative attributes. The results obtained were statistically analysed and revealed that the proposed algorithm outperformed known classifiers, e.g. C4.5, Naive Bayes, and SVM.
Keywords:computational intelligence, particle swarm optimization, feature selection, classification


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