diplomsko delo visokošolskega strokovnega študijskega programa
Andreja Zacirkovnik (Author), Boštjan Brumen (Mentor)

Abstract

Diplomsko delo vsebuje teoretično osnovo strojnega učenja, podrobnejši opis podatkovnega rudarjenja ter njegove metode, natančneje klasifikacije. V drugem, praktičnem delu smo opisali tri prosto dostopna orodja, ki podpirajo strojno učenje ter metodo klasifikacije. Orodja smo testirali ter izmerili njihovo natančnost klasifikacije s statistično metodo cross-validation oz. prečnim preverjanjem klasifikacijske točnosti. Obravnavana in analizirana orodja, ki podpirajo metode strojnega učenja, so bila Weka, See5 in GATree. Rezultati analize so pokazali, da je od le teh najbolj natančno orodje za izvajanje klasifikacije program Weka.

Keywords

strojno učenje;podatkovno rudarjenje;klasifikacija;orodje;Cross-validation;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: [A. Zacirkovnik]
UDC: 004.4'24:004.85(043.2)
COBISS: 17504022 Link will open in a new window
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Downloads: 794
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Other data

Secondary language: English
Secondary title: TOOLS FOR MACHINE LEARNING - CLASSIFICATION
Secondary abstract: Diploma paper contains theoretical basis of machine learning, a more detailed description of the data mining and its methods, specifically classification. In the second, practical part, we describe three tools that are available free and which support the machine learning and classification method. Tools were tested and measured the accuracy of the classification by the statistical method and cross-validation or cross-checking the classification accuracy. Discussed and analyzed tools that support the machine learning methods have been Weka, See5 and GATree. Results of the analysis showed that among these tools, the program Weka is the most accurate for the implementation of the classification.
Secondary keywords: machine learning;dta mining;classification;tools;cross-validation;
URN: URN:SI:UM:
Type (COBISS): Bachelor thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko
Pages: IX, 52 str.
ID: 8727055
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