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Feature selection is a preprocessing step which plays a crucial role in thedomain of machine learning and data mining. Feature selection methods have beenshown to be effctive in removing redundant and irrelevant features, improvingthe learning algorithm's prediction performance. Among the various methods offeature selection based on redundancy, the fast correlation-based filter (FCBF)is one of the most effective. In this paper, we proposed a novel extension ofFCBF, called RFCBF, which combines resampling technique to improveclassification accuracy. We performed comprehensive experiments to compare theRFCBF with other state-of-the-art feature selection methods using the KNNclassifier on 12 publicly available data sets. The experimental results showthat the RFCBF algorithm yields significantly better results than previousstate-of-the-art methods in terms of classification accuracy and runtime.

RFCBF: enhance the performance and stability of Fast Correlation-Based Filter