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