RFCBF: enhance the performance and stability of Fast Correlation-Based Filter
This work addresses feature selection for machine learning practitioners, offering incremental improvements in performance and stability.
The authors tackled the problem of feature selection by extending the Fast Correlation-Based Filter (FCBF) with a resampling technique, resulting in RFCBF, which achieved significantly better classification accuracy and runtime compared to state-of-the-art methods on 12 datasets.
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.