Ranking attributes using learning of preferences by means of SVM

• Hernández Arauzo, Alejandro ^[1] ; García Torres, Miguel ^[2] ; Bahamonde, Antonio ^[1]
  1. [1] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  2. [2] Universidad de La Laguna

     Universidad de La Laguna

     San Cristóbal de La Laguna, España

     
• Localización: XII Conferencia de la Asociación Española para la Inteligencia Artificial: (CAEPIA 2007). Actas / coord. por Daniel Borrajo Millán , Luis Castillo Vidal , Juan Manuel Corchado Rodríguez , Vol. 1, 2007, ISBN 978-84-611-8847-5, págs. 87-96
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • A relaxed setting for Feature Selection is known as Feature Ranking in Machine Learning. The aim is to establish an order between the attributes that describe the entries of a learning task according to their utility. In this paper, we propose a method to establish these orders using Preference Learning by means of Support Vector Machines (SVM). We include an exhaustive experimental study that investigates the virtues and limitations of the method and discusses, simultaneously, the design options that we have adopted. The conclusion is that our method is very competitive, specially when it searchs for a ranking limiting the number of combinations of attributes explored; this supports that the method presented here could be successfully used in large data sets.