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Fisher score-based feature selection for ordinal classification: a social survey on subjective well-being

Perez-Ortiz, Maria, Torres-Jimenez, Mercedes, Antonio Gutierrez, Pedro, Sanchez-Monedero, Javier and Hervas-Martinez, Cesar 2016. Fisher score-based feature selection for ordinal classification: a social survey on subjective well-being. Presented at: HAIS 2016: 11th International Conference on Hybrid Artificial Intelligence Systems, Seville, Spain, 18-20 April 2016. Published in: Martinez-Alvarez, Francisco, Troncoso, Alicia, Quintian, Hector and Corchado, Emilio eds. Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings. Lecture Notes in Computer Science Cham: Springer, pp. 597-608. 10.1007/978-3-319-32034-2_50

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Journalism, Media and Culture
Publisher: Springer
ISBN: 978-3-319-32034-2
Date of First Compliant Deposit: 6 July 2018
Last Modified: 02 Aug 2019 10:05

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