Título Fisher Score-Based Feature Selection for Ordinal Classification: A Social Survey on Subjective Well-Being
Autores PÉREZ ORTIZ, MARÍA, TORRES JIMÉNEZ, MERCEDES, Antonio Gutierrez, Pedro , SÁNCHEZ MONEDERO, JAVIER, Hervas-Martinez, Cesar
Publicación externa No
Medio Lecture Notes in Computer Science
Alcance Proceedings Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.339
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964072872&doi=10.1007%2f978-3-319-32034-2_50&partnerID=40&md5=a96235bdff78457642f3516e3eb09b8f
Fecha de publicacion 01/01/2016
ISI 000389499600050
Scopus Id 2-s2.0-84964072872
DOI 10.1007/978-3-319-32034-2_50
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.
Palabras clave Artificial intelligence; Intelligent systems; Surveys; Community wells; Daily activity; Fisher score; Input variables; Ordinal classification; Ordinal features; Social survey; Social well-being; Feature extraction
Miembros de la Universidad Loyola

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