Municipal Revenue Prediction by Support Vector Machine Ensembles

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F10%3A39881985" target="_blank" >RIV/00216275:25410/10:39881985 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Municipal Revenue Prediction by Support Vector Machine Ensembles

Original language description

Fiscal stress has forced municipalities to pay increasing attention to the importance of revenue prediction. Currently, econometric models and expert opinions are used for municipal revenue prediction. In this paper we present a design of support vectormachine ensembles for the prediction of municipal revenue. Linear regression model and feed-forward neural network ensembles are used as benchmark methods. We prove that stochastic gradient boosting outperforms the other methods when creating SVM ensembles for this regression problem. Further, bagging shows best performance for feed-forward neural network ensembles, and dagging is preferable for linear regression model ensembles.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

AE - Management, administration and clerical work

OECD FORD branch

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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)

Publication year

2010

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Latest Trends on Computers

ISBN

978-960-474-201-1

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

WSEAS Press

Place of publication

Atény

Event location

Corfu

Event date

Jul 23, 2010

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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