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  • 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 vector machine 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.
  • 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 vector machine 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. (en)
Title
  • Municipal Revenue Prediction by Support Vector Machine Ensembles
  • Municipal Revenue Prediction by Support Vector Machine Ensembles (en)
skos:prefLabel
  • Municipal Revenue Prediction by Support Vector Machine Ensembles
  • Municipal Revenue Prediction by Support Vector Machine Ensembles (en)
skos:notation
  • RIV/00216275:25410/10:39881985!RIV11-GA0-25410___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA402/08/0849), P(GP402/09/P090)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 273232
http://linked.open...ai/riv/idVysledku
  • RIV/00216275:25410/10:39881985
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • neural network ensembles; modelling; support vector machine ensembles; regression; prediction; Municipal revenue (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [AF312899154E]
http://linked.open...v/mistoKonaniAkce
  • Corfu
http://linked.open...i/riv/mistoVydani
  • Atény
http://linked.open...i/riv/nazevZdroje
  • Latest Trends on Computers
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hájek, Petr
  • Olej, Vladimír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • WSEAS Press
https://schema.org/isbn
  • 978-960-474-201-1
http://localhost/t...ganizacniJednotka
  • 25410
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