Correlation based feature selection method

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.