Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature subset selection for learning preferences: a case study
ICML '04 Proceedings of the twenty-first international conference on Machine learning
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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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.