|
|
Data Classification Methods
Kaščák, Pavol ; Šebek, Michal (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis deals with data classification, focusing on the implementation of naive Bayes classification method. At First, it is generally described process of data classification, its division into phases with their characteristic. It is followed by a more accurate description of the naive Bayes classification method and description of the implementation by using Java programming language and MySQL database. The last section contains a summary of the results.
|
|
|
Statistical Classification Methods
Barvenčík, Oldřich ; Žák, Libor (referee) ; Michálek, Jaroslav (advisor)
The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.
|
|
|
Comparison of selected classification methods for multivariate data
Stecenková, Marina ; Řezanková, Hana (advisor) ; Berka, Petr (referee)
The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.
|
|
|
Prediction of firm financial distress
ZDENĚK, Radek
The aim of the doctoral thesis is to screen possibilities of multivariate classification methods used for the prediction of a financial distress of agricultural enterprises. Application of the thesis was based on a definition of an enterprise threatened by financial distress defined according to relevant literature review. The reliability of current classification models was verified first as a part of the solution process. The ability of each indicator and their combinations in terms of reliability classification were assessed as well. The main part consisted in the construction of models using classification methods (linear and quadratic discriminant analysis and robust variants, the methods of nearest neighbours and prototypes, logistic regression, probit regression, multilayer perceptron networks, classification trees and forests).
|
guest ::
