Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Fisher Score-Based Feature Selection for Ordinal Classification: A Social Survey on Subjective Well-Being
verfasst von : María Pérez-Ortiz, Mercedes Torres-Jiménez, Pedro Antonio Gutiérrez, Javier Sánchez-Monedero, César Hervás-Martínez
Erschienen in: Hybrid Artificial Intelligent Systems
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.