Title
Izbor atributa integracijom znanja o domenu primenom metoda odlučivanja kod prediktivnog modelovanja vremenskih serija nadgledanim mašinskim učenjem
Creator
Marković, Ivana P. 1979-
Copyright date
2017
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 03.05.2018.
Other responsibilities
mentor
Stanković, Milena
član komisije
Stoimenov, Leonid
član komisije
Stojković, Suzana
član komisije
Stanković, Miomir
član komisije
Milovanović, Slavoljub
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Nišu
Faculty
Elektronski fakultet
Group
Katedra za računarstvo
Alternative title
Feature selection by integrating domain-specific knowledge using decision making methods for predicitive modeling of time-series data with supervised machine learning methods
Publisher
[I. P. Marković]
Format
VI, 123 lista
description
Biografija autora: list [124];
Bibliografija: listovi 108-123
description
Data mining – Machine learning
Abstract (en)
The aim of the research presented within this doctoral dissertation is
to develop a feature selection methodology through integrating
domain-specific knowledge by applying mathematical methods of
decision-making, to improve the feature selection process and the
precision of supervised machine learning methods for predictive
modeling of time series.
To integrate domain-specific knowledge, a multi-criteria decision
making method is used, i.e. an analytical hierarchical process proven
to be successful in numerous studies carried out to date. This
approach was selected because it allows the selection of a set of
factors based on their relevance, even in the case of mutually opposite
criteria.
In predicting the movement of time series, the possibility of
integrating feature relevance into support vector machines to improve
their prediction accuracy was studied.
The proposed methodology was applied as a feature-selection method
for the predictive modelling of movement of financial time series.
Unlike existing approaches, where the feature selection method is
based on a quantitative analysis of the input values, the proposed
methodology carries out a qualitative evaluation of the attributes in
relation to the prediction domain and represents a means of
integrating a priori knowledge of the prediction domain.
Authors Key words
izbor atributa, težinska kernel funkcija, prediktivno modelovanje,
vremenske serije
Authors Key words
Feature selection, Weighted kernel function, Predictive modeling,
Time series
Classification
(004.431.2:532.5):004.414.22(043.3)
Subject
Т120
Type
Elektronska teza
Abstract (en)
The aim of the research presented within this doctoral dissertation is
to develop a feature selection methodology through integrating
domain-specific knowledge by applying mathematical methods of
decision-making, to improve the feature selection process and the
precision of supervised machine learning methods for predictive
modeling of time series.
To integrate domain-specific knowledge, a multi-criteria decision
making method is used, i.e. an analytical hierarchical process proven
to be successful in numerous studies carried out to date. This
approach was selected because it allows the selection of a set of
factors based on their relevance, even in the case of mutually opposite
criteria.
In predicting the movement of time series, the possibility of
integrating feature relevance into support vector machines to improve
their prediction accuracy was studied.
The proposed methodology was applied as a feature-selection method
for the predictive modelling of movement of financial time series.
Unlike existing approaches, where the feature selection method is
based on a quantitative analysis of the input values, the proposed
methodology carries out a qualitative evaluation of the attributes in
relation to the prediction domain and represents a means of
integrating a priori knowledge of the prediction domain.
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