A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Correlation-based Feature Selection Strategy in Neural Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Pairwise feature evaluation for constructing reduced representations
Pattern Analysis & Applications
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
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Feature selection is an important data preprocessing step which is performed before a learning algorithm is applied. The issue that has to be taken into consideration when proposing a feature selection method is its computational complexity. Often, if the feature selection process is fast, it cannot thoroughly search the feature subset space and classification accuracy is degraded. Lately, a pairwise feature selection method was proposed as an effective trade-off between computation speed and classification accuracy. In this paper, a new feature selection method is proposed which further improves feature selection speed while preserving classification accuracy. The new method selects features individually or in a pairwise manner based on the correlations between features. Experiments conducted on several benchmark data sets prove with high statistical significance that the correlation-based feature selection method shortens computations compared to the pairwise feature selection method and produces classification errors that are not worse than those produced by existing methods.