Show simple item record

Feature subset selection for learning preferences: a case study

dc.contributor.authorBahamonde Rionda, Antonio 
dc.contributor.authorFernández Bayón, Gustavo 
dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorCoz Velasco, Juan José del 
dc.contributor.authorAlonso González, Jaime 
dc.contributor.authorGoyache, Félix
dc.description.abstractIn 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 alternativesspa
dc.relation.ispartofProceedings of the twenty-first international conference on Machine learningspa
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.titleFeature subset selection for learning preferences: a case studyspa

Files in this item


This item appears in the following Collection(s)

Show simple item record

CC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
This item is protected with a Creative Commons License