A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Feature Selection Using Partial Correlation for Multi-vaslued Attributes
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A General Framework of Feature Selection for Text Categorization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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The feature selection allows to choose P features among M (PM) and thus to reduce the representation space of data. This process is increasingly useful because of the databases size increase. Therefore we propose a method based on preferences aggregation. It is an hybrid method between filter and wrapper approaches.