A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Feature selection based on loss-margin of nearest neighbor classification
Pattern Recognition
Consensus group stable feature selection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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The Journal of Machine Learning Research
A Variance Reduction Framework for Stable Feature Selection
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Variable and feature selection has been a research topic with practical significance in many areas such as statistics, pattern recognition, machine learning and data mining. The task of feature selection is to choose an effective feature subset out of a given feature set to reduce the feature space dimensionality. In this paper, along with the guidelines of Energy-based model, a unified energy-based framework for feature selection and a feature ranking algorithm under this framework is presented. On the other hand, in order to increase the stability of our algorithm, an ensemble feature selection is introduced. Some experiments are conducted on the real world and synthesis data sets to demonstrate the ability of our feature selection algorithm and the stability improvement of the ensemble feature selection.