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One-Class Classification to Predict the Success of Private-Participation Infrastructure Projects in Europe

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14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) (SOCO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 950))

Abstract

Foreign investment has significantly increased in infrastructure projects as governments have realized the potential advantages for the host countries in terms of capital and technical expertise. Private participation projects are a common vehicle for private firms to invest in infrastructures, although these projects are typically subject to pressures from the government, consumers, suppliers, regulatory institutions, and public opinion. Forecasting the success of these projects in advance is a key element to be taken into account when deciding about participation. To support this kind of decisions, present paper proposes the application of some one-class classifiers to check their ability to predict the final success of private participation projects involving infrastructures. To validate the proposed soft-computing models, they are applied to a real-life dataset from the World Bank, comprising information about projects in European countries within the Energy and Telecommunication sectors.

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Author information

Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Álvaro Herrero

  2. Department of Management, KEDGE Business School, Bordeaux, France

    Alfredo Jiménez

Authors
  1. Álvaro Herrero
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  2. Alfredo Jiménez
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Corresponding author

Correspondence to Álvaro Herrero .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Herrero, Á., Jiménez, A. (2020). One-Class Classification to Predict the Success of Private-Participation Infrastructure Projects in Europe. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_42

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