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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.
  • 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. (en)
Title
  • Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines
  • Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines (en)
skos:prefLabel
  • Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines
  • Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines (en)
skos:notation
  • RIV/00216275:25410/10:39881977!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...iv/cisloPeriodika
  • 11
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
  • 273231
http://linked.open...ai/riv/idVysledku
  • RIV/00216275:25410/10:39881977
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...odStatuVydavatele
  • GR - Řecká republika
http://linked.open...ontrolniKodProRIV
  • [3478207FD325]
http://linked.open...i/riv/nazevZdroje
  • WSEAS Transactions 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...v/svazekPeriodika
  • 9
http://linked.open...iv/tvurceVysledku
  • Hájek, Petr
  • Olej, Vladimír
issn
  • 1109-2750
number of pages
http://localhost/t...ganizacniJednotka
  • 25410
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