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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/31230

Title: Feature subset selection for learning preferences: a case study
Author(s): Bahamonde Rionda, Antonio
Fernández Bayón, Gustavo
Díez Peláez, Jorge
Quevedo Pérez, José Ramón
Luaces Rodríguez, Óscar
Coz Velasco, Juan José del
Alonso González, Jaime
Goyache, Félix
Issue date: 2004
Publisher: ACM
Publisher version: http://dx.doi.org/10.1145/1015330.1015378
Abstract: In this paper we tackle a real world problem, the search of a function to evaluate the merits of beef cattle as meat producers. The independent variables represent a set of live animals’ measurements; while the outputs cannot be captured with a single number, since the available experts tend to assess each animal in a relative way, comparing animals with the other partners in the same batch. Therefore, this problem can not be solved by means of regression methods; our approach is to learn the preferences of the experts when they order small groups of animals. Thus, the problem can be reduced to a binary classifi- cation, and can be dealt with a Support Vector Machine (SVM) improved with the use of a feature subset selection (FSS) method. We develop a method based on Recursive Feature Elimination (RFE) that employs an adaptation of a metric based method devised for model selection (ADJ). Finally, we discuss the extension of the resulting method to more general settings, and provide a comparison with other possible alternatives
URI: http://hdl.handle.net/10651/31230
ISBN: 1-58113-838-5
Appears in Collections:Ponencias, Discursos y Conferencias
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