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Fisher Score-Based Feature Selection for Ordinal Classification: A Social Survey on Subjective Well-Being

  • María Pérez-OrtizEmail author
  • Mercedes Torres-Jiménez
  • Pedro Antonio Gutiérrez
  • Javier Sánchez-Monedero
  • César Hervás-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

This paper approaches the problem of feature selection in the context of ordinal classification problems. To do so, an ordinal version of the Fisher score is proposed. We test this new strategy considering data from an European social survey concerning subjective well-being, in order to understand and identify the most important variables for a person’s happiness, which is represented using ordered categories. The input variables have been chosen according to previous research, and these have been categorised in the following groups: demographics, daily activities, social well-being, health and habits, community well-being and personality/opinion. The proposed strategy shows promising results and performs significantly better than its nominal counterpart, therefore validating the need of developing specific ordinal feature selection methods. Furthermore, the results of this paper can shed some light on the human psyche by analysing the most and less frequently selected variables.

Keywords

Feature Selection Feature Selection Method Hausdorff Distance Mean Absolute Error European Social Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • María Pérez-Ortiz
    • 1
    Email author
  • Mercedes Torres-Jiménez
    • 1
  • Pedro Antonio Gutiérrez
    • 2
  • Javier Sánchez-Monedero
    • 1
  • César Hervás-Martínez
    • 2
  1. 1.Department of Quantitative MethodsUniversidad Loyola AndalucíaCórdobaSpain
  2. 2.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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