Filter methods for feature selection: a comparative study

Authors:
Noelia Sánchez-Maroño;Amparo Alonso-Betanzos;María Tombilla-Sanromán
Affiliations:
University of A Coruña, Department of Computer Science, A Coruña, Spain;University of A Coruña, Department of Computer Science, A Coruña, Spain;University of A Coruña, Department of Computer Science, A Coruña, Spain
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 8
Cited 2

A practical approach to feature selection

ML92 Proceedings of the ninth international workshop on Machine learning
On the Handling of Continuous-Valued Attributes in Decision Tree Generation

Machine Learning
Estimating attributes: analysis and extensions of RELIEF

ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Theoretical and Empirical Analysis of ReliefF and RReliefF

Machine Learning
Evolving Feature Selection

IEEE Intelligent Systems
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Searching for interacting features

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

Implementing ReliefF filters to extract meaningful features from genetic lifetime datasets

Journal of Biomedical Informatics
Efficient feature size reduction via predictive forward selection

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Adequate selection of features may improve accuracy and efficiency of classifier methods. There are two main approaches for feature selection: wrapper methods, in which the features are selected using the classifier, and filter methods, in which the selection of features is independent of the classifier used. Although the wrapper approach may obtain better performances, it requires greater computational resources. For this reason, lately a new paradigm, hybrid approach, that combines both filter and wrapper methods has emerged. One of its problems is to select the filter method that gives the best relevance index for each case, and this is not an easy to solve question. Different approaches to relevance evaluation lead to a large number of indices for ranking and selection. In this paper, several filter methods are applied over artificial data sets with different number of relevant features, level of noise in the output, interaction between features and increasing number of samples. The results obtained for the four filters studied (ReliefF, Correlation-based Feature Selection, Fast Correlated Based Filter and INTERACT) are compared and discussed. The final aim of this study is to select a filter to construct a hybrid method for feature selection.