PÉREZ ORTIZ, MARÍA, TORRES JIMÉNEZ, MERCEDES, Antonio Gutierrez, Pedro , SÁNCHEZ MONEDERO, JAVIER, Hervas-Martinez, Cesar
No
Lect. Notes Comput. Sci.
Proceedings Paper
Científica
0.339
01/01/2016
000389499600050
2-s2.0-84964072872
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.
Artificial intelligence; Intelligent systems; Surveys; Community wells; Daily activity; Fisher score; Input variables; Ordinal classification; Ordinal features; Social survey; Social well-being; Feature extraction