Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F10%3A39881977" target="_blank" >RIV/00216275:25410/10:39881977 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines
Original language description
Municipalities have to to pay increasing attention to the importance of revenue prediction due to fiscal stress. Currently, judgmental, extrapolative, and deterministic models are used for municipal revenue prediction. In this paper we present the designs of neural network and support vector machine ensembles for a real-world regression problem, i.e. prediction of municipal revenue. Base learners, as well as linear regression models are used as benchmark methods. We prove that there is no single ensemble method suitable for this regression problem. However, the ensembles of support vector machines and neural networks outperformed the base learners and linear regression models significantly.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AE - Management, administration and clerical work
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
WSEAS Transactions on Computers
ISSN
1109-2750
e-ISSN
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Volume of the periodical
9
Issue of the periodical within the volume
11
Country of publishing house
GR - GREECE
Number of pages
10
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
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