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Prioritization of Public Expenditure for a Better Return on Social Development: A Data Mining Approach

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Advanced Machine Learning Technologies and Applications (AMLTA 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 488))

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Abstract

Public expenditure affects people both directly, through subsidies and transfers, and indirectly through affecting consumption and production activities. The effects of public expenditure depend not only on its absolute values but also on both its composition and the efficiency of this spending. This paper uses data mining techniques to reach a model that maximizes social develoment through efficient allocation of public expenditure and assesses the current state of Egypt with respect to the model reached. Out of five tested models, decision tree was the one found more appropriate given this research focus and data available.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Hisham M. Abdelsalam, Abdoulrahman Al-shaar, Areej M. Zaki, Mohamed Saleh & Miral H. khodeir

  2. Faculty of Economics and Political Science, Cairo University, Cairo, Egypt

    Nahla El-Sebai

Authors
  1. Hisham M. Abdelsalam
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  2. Abdoulrahman Al-shaar
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  3. Areej M. Zaki
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  4. Nahla El-Sebai
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  5. Mohamed Saleh
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  6. Miral H. khodeir
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Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Benha University, Benha, Egypt

    Ahmad Taher Azar

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© 2014 Springer International Publishing Switzerland

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Abdelsalam, H.M., Al-shaar, A., Zaki, A.M., El-Sebai, N., Saleh, M., khodeir, M.H. (2014). Prioritization of Public Expenditure for a Better Return on Social Development: A Data Mining Approach. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_48

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  • DOI: https://doi.org/10.1007/978-3-319-13461-1_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13460-4

  • Online ISBN: 978-3-319-13461-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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