Thesis Details
Analýza klasifikačních metod
This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.
data mining, knowledge, classification, analysis, data set, neural network, SVM, Naive Bayes, decision tree, machine learning, desktop application, experiments, accuracy, duration
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Horák Aleš, doc. RNDr., Ph.D. (FI MUNI), člen
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT21692,
author = "Jakub Jur\'{i}\v{c}ek",
type = "Master's thesis",
title = "Anal\'{y}za klasifika\v{c}n\'{i}ch metod",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2019,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/21692/"
}