Feature Subset Selection for Learning Preferences: A Case Study
Antonio Bahamonde - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Gustavo Bayón - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Jorge Díez - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain José R. Quevedo - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Oscar Luaces - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Juan José del Coz - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Jaime Alonso - Centro de Inteligencia Artificial. Universidad de Oviedo at Gijón, Spain Félix Goyache - SERIDA-CENSYRA-Somió, Spain
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.